16
Research Article Model for Vaccine Design by Prediction of B-Epitopes of IEDB Given Perturbations in Peptide Sequence, In Vivo Process, Experimental Techniques, and Source or Host Organisms Humberto González-Díaz, 1,2 Lázaro G. Pérez-Montoto, 3 and Florencio M. Ubeira 3 1 Department of Organic Chemistry II, University of the Basque Country UPV/EHU, 48940 Bilbao, Spain 2 Ikerbasque, Basque Foundation for Science, 48011 Bilbao, Spain 3 Department of Microbiology and Parasitology, University of Santiago de Compostela (USC), 15782 Santiago de Compostela, Spain Correspondence should be addressed to Humberto Gonz´ alez-D´ ıaz; [email protected] Received 21 August 2013; Accepted 17 November 2013; Published 12 January 2014 Academic Editor: Darren R. Flower Copyright © 2014 Humberto Gonz´ alez-D´ ıaz et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Perturbation methods add variation terms to a known experimental solution of one problem to approach a solution for a related problem without known exact solution. One problem of this type in immunology is the prediction of the possible action of epitope of one peptide aſter a perturbation or variation in the structure of a known peptide and/or other boundary conditions (host organism, biological process, and experimental assay). However, to the best of our knowledge, there are no reports of general-purpose perturbation models to solve this problem. In a recent work, we introduced a new quantitative structure-property relationship theory for the study of perturbations in complex biomolecular systems. In this work, we developed the first model able to classify more than 200,000 cases of perturbations with accuracy, sensitivity, and specificity >90% both in training and validation series. e perturbations include structural changes in >50000 peptides determined in experimental assays with boundary conditions involving >500 source organisms, >50 host organisms, >10 biological process, and >30 experimental techniques. e model may be useful for the prediction of new epitopes or the optimization of known peptides towards computational vaccine design. 1. Introduction National Institute of Allergy and Infectious Diseases (NIAID) supported the launch, in 2004, of the Immune Epitope Data- base (IEDB), http://www.iedb.org/ [14]. e IEDB system withdrew information from approximately 99% of all papers published to date that describe immune epitopes. In doing so, IEDB system analyses over 22 million PubMed abstracts and subsequently curated 13 K references, including 7K manuscripts about infectious diseases, 1 K about allergy top- ics, 4 K about autoimmunity, and 1 K about transplant/allo- antigen topics [5]. IEDB lists a huge amount of information about the molecular structure as well as the experimental conditions ( ) in which different th molecules were deter- mined to be immune epitopes or not. is explosion of infor- mation makes necessary both query/display functions for retrieval of known data from IEDB as well predictive tools for new epitopes. Salimi et al. [5] reviewed advances in epitope analysis and predictive tools available in the IEDB. In fact, IEDB analysis resource (IEDB-AR: http://tools.iedb.org/) is a collection of tools for prediction of molecular targets of T- and B-cell immune responses (i.e., epitopes) [6, 7]. On the other hand, Quantitative Structure-Activity/Prop- erty Relationships (QSAR/QSPR) techniques are useful tool to predict new drugs, RNA, drug-protein complexes, and pro- tein-protein complexes. In general, QSAR/QSPR-like meth- ods transform molecular structures into numeric molecular descriptors ( ) in a first stage and later fit a model to pre- dict the biological process. For example, DRAGON [810], CODESSA [11, 12], MOE [13], TOPS-MODE [1417], TOMO- COMD [18, 19], and MARCH-INSIDE [20] are among the most used soſtwares to calculate molecular descriptors based on quantum mechanics (QM) and/or graph theory [2127]. e soſtware STATISTICA [28] and WEKA [29] are oſten Hindawi Publishing Corporation Journal of Immunology Research Volume 2014, Article ID 768515, 15 pages http://dx.doi.org/10.1155/2014/768515

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Page 1: Research Article Model for Vaccine Design by …downloads.hindawi.com/journals/jir/2014/768515.pdfResearch Article Model for Vaccine Design by Prediction of B-Epitopes of IEDB Given

Research ArticleModel for Vaccine Design by Prediction of B-Epitopes ofIEDB Given Perturbations in Peptide Sequence In Vivo ProcessExperimental Techniques and Source or Host Organisms

Humberto Gonzaacutelez-Diacuteaz12 Laacutezaro G Peacuterez-Montoto3 and Florencio M Ubeira3

1 Department of Organic Chemistry II University of the Basque Country UPVEHU 48940 Bilbao Spain2 Ikerbasque Basque Foundation for Science 48011 Bilbao Spain3 Department of Microbiology and Parasitology University of Santiago de Compostela (USC) 15782 Santiago de Compostela Spain

Correspondence should be addressed to Humberto Gonzalez-Dıaz humbertogonzalezdiazehues

Received 21 August 2013 Accepted 17 November 2013 Published 12 January 2014

Academic Editor Darren R Flower

Copyright copy 2014 Humberto Gonzalez-Dıaz et al This is an open access article distributed under the Creative CommonsAttribution License which permits unrestricted use distribution and reproduction in any medium provided the original work isproperly cited

Perturbation methods add variation terms to a known experimental solution of one problem to approach a solution for a relatedproblemwithout known exact solutionOne problemof this type in immunology is the prediction of the possible action of epitope ofone peptide after a perturbation or variation in the structure of a known peptide andor other boundary conditions (host organismbiological process and experimental assay) However to the best of our knowledge there are no reports of general-purposeperturbation models to solve this problem In a recent work we introduced a new quantitative structure-property relationshiptheory for the study of perturbations in complex biomolecular systems In this work we developed the first model able to classifymore than 200000 cases of perturbations with accuracy sensitivity and specificity gt90 both in training and validation seriesThe perturbations include structural changes in gt50000 peptides determined in experimental assays with boundary conditionsinvolving gt500 source organisms gt50 host organisms gt10 biological process and gt30 experimental techniques The model maybe useful for the prediction of new epitopes or the optimization of known peptides towards computational vaccine design

1 Introduction

National Institute of Allergy and InfectiousDiseases (NIAID)supported the launch in 2004 of the Immune Epitope Data-base (IEDB) httpwwwiedborg [1ndash4] The IEDB systemwithdrew information from approximately 99 of all paperspublished to date that describe immune epitopes In doingso IEDB system analyses over 22 million PubMed abstractsand subsequently curated asymp13 K references including asymp7Kmanuscripts about infectious diseasesasymp1 K about allergy top-ics asymp4K about autoimmunity and 1 K about transplantallo-antigen topics [5] IEDB lists a huge amount of informationabout the molecular structure as well as the experimentalconditions (119888

119894119895) in which different 119894th molecules were deter-

mined to be immune epitopes or notThis explosion of infor-mation makes necessary both querydisplay functions forretrieval of known data from IEDB as well predictive tools for

new epitopes Salimi et al [5] reviewed advances in epitopeanalysis and predictive tools available in the IEDB In factIEDB analysis resource (IEDB-AR httptoolsiedborg) is acollection of tools for prediction of molecular targets of T-and B-cell immune responses (ie epitopes) [6 7]

On the other hand Quantitative Structure-ActivityProp-erty Relationships (QSARQSPR) techniques are useful toolto predict newdrugs RNA drug-protein complexes and pro-tein-protein complexes In general QSARQSPR-like meth-ods transform molecular structures into numeric moleculardescriptors (120582

119894) in a first stage and later fit a model to pre-

dict the biological process For example DRAGON [8ndash10]CODESSA [11 12]MOE [13] TOPS-MODE [14ndash17] TOMO-COMD [18 19] and MARCH-INSIDE [20] are among themost used softwares to calculate molecular descriptors basedon quantum mechanics (QM) andor graph theory [21ndash27]The software STATISTICA [28] and WEKA [29] are often

Hindawi Publishing CorporationJournal of Immunology ResearchVolume 2014 Article ID 768515 15 pageshttpdxdoiorg1011552014768515

2 Journal of Immunology Research

used to performmultivariate statistics andor machine learn-ing (ML) analysis in order to preprocess data and later fitthe final QSARQSPR model using techniques like princi-pal component analysis (PCA) linear discriminant analysis(LDA) support vector machine (SVM) or artificial neuralnetworks (ANN) [28]

QSARQSPRmodels are also important in immunoinfor-matics to predict the propensity of different molecular struc-tures to play different roles in immunological processesTheyinclude skin vaccine adjuvants and sensitizers [30ndash38] drugsand their activitytoxicity protein targets in the immunesystem [39] and epitopes [40ndash49] Moreover Reche andReinherz [50] implemented PEPVAC (promiscuous epitope-based vaccine) a web server for the formulation of multi-epitope vaccines that predict peptides binding to five distinctHLA class I supertypes (A2 A3 B7 A24 and B15) PEPVACcan also identify conserved MHC ligands as well as thosewith a C-terminus resulting from proteasomal cleavage TheDana-Farber Cancer Institute hosted the PEPVAC server atthe site httpimmunaxdfciharvardeduPEPVAC To closewith a last example Lafuente and Reche [51] reviewed theavailable methods for predicting MHC-peptide binding anddiscussed their most relevant advantages and drawbacks

In many complex QSPR-like problems in immunoinfor-matics like in other areas we know the exact experimentalresult (known solution) of the problem but we are interestedin the possible result obtained after a change (perturbation)on one or multiple values of the initial conditions of theexperiment (new solution) For instance we often know forlarge collections of 119894th molecules (119898

119894) organic compounds

drugs xenobiotics andor peptide sequences the efficiencyof the compound 120576(119888

119894119895) as adjuvant action as epitope immun-

otoxicity andor the interaction (affinity inhibition etc) withimmunological targets In addition we often known for eachmolecule the exact conditions (119888

119894119895) of assay for the initial

experiment including structure of the molecule 119898119894(drug

adjuvant and sequence of the peptide) source organism (so)host organism (ho) immunological process (ip) experimen-tal technique (tq) concentration temperature time solventsand coadjuvants This is the case of big data retrieved fromvery large databases like IEDB [1ndash4] and CHEMBL [52]However we do not know the possible result of the experi-ment if we change at least one of these conditions (perturba-tion) We refer to small changes or perturbations in bothstructure and condition for input or output variables Itmeans that we include changes in ho so ip and tq changes ofthe compound by one analogue compound with similarstructure changes in the sequence of the epitope (artificial byorganic synthesis or natural mutations) and polarity of thesolvent or coadjuvants In these cases we could use a per-turbation theory model to solve the QSARQSPR problemPerturbation theory includes methods that add ldquosmallrdquo termsto a known solution of a problem in order to approach asolution to a related problemwithout known solution Pertur-bation models have been widely used in all branches ofscience from QM to astronomy and life sciences includingchaos or ldquobutterfly effectrdquo Bohrrsquos atomic theory Heisenbergrsquosmechanics Zeemanrsquos and Starkrsquos effects and other models

with applications in like protein spectroscopy and others [53ndash57] In a very recent work Gonzalez-Diaz et al [58] formu-lated a general-purpose perturbation theory or model formultiple-boundaryQSPRQSARproblems However there isnot report in the immunoinformatics literature of a generalQSPR perturbation model for IEDB B-epitopes Here wereport the first example of QSPR-perturbation model for B-epitopes reported in IEDB able to predict the probability ofoccurrence of an epitope after a perturbation in the sequencethe experimental technique the exposition process andorthe source or host organisms

2 Materials and Methods

21 Molecular Descriptors for Peptides We calculated themolecular descriptors of the structure of peptides using thesoftwareMARCH-INSIDE (MI) based on the algorithmwiththe same name [59] The MI approach uses a Markov Chainmethod to calculate the 119896th mean values of different physico-chemical molecular properties 120582(119898

119894) for 119894th molecules (119898)

These 120582(119898119894) values are calculated as an average of 119896120582(119898119894)

values for all atoms placed at topological distance 119889 le 119896which are in turn the means of atomic properties (120582

119895) for all

atoms in the molecule and its neighbors placed at 119889 = 119896For instance it is possible to derive average estimationsof molecular refractivities 119896MR(119898

119894) partition coefficients

119896119875(119898119894) and hardness 119896120578(119898

119894) for atoms placed at different

topological distances 119889 le 119896 In this first work we calculatedonly one type of 120582(119898

119894) values We calculated for all peptides

the average value 120594(119898119894) of all the atomic electronegativities 120594

119894

for all 120575119894atoms connected to the 119894th atom (119894 rarr 119895) and their

neighbors placed at a distance 119889 le 5 [59]

120594 (119898119894) =

1

6

5

sum

119896=0

119896120594119895=

1

6

5

sum

119896=0

120575119894

sum

119894rarr 119895

119901119896(120594119895) sdot 120594119895 (1)

We calculate the probabilities 119896119901(120582119895) for any atomic prop-

erty including 119896119901(120594119895) using a Markov Chain model for the

gradual effects of the neighboring atoms at different distancesin the molecular backbone This method has been explainedin detail in many previous works so we omit the details here[59]

22 Electronegativity Perturbation Model for Prediction of B-Epitopes Very recently Gonzalez-Diaz et al [58] formulateda general-purpose perturbation theory ormodel formultiple-boundary QSPRQSAR problems We adapted here this newtheory or modeling method to approach to the peptide pre-diction problem from the point of view of perturbation the-ory Let be a set of 119894th peptide molecules denoted as119898

119894with a

value of efficiency 120576119894119895as epitopes experimentally determined

under a set of boundary conditions 119888119895equiv (1198880 1198881 1198882 1198883 119888

119899)

We put the main emphasis here on peptides reported in thedatabase IEDB In this sense the boundary conditions 119888

119895used

here are the same reported in this database 1198880= is the specific

Journal of Immunology Research 3

peptide 1198881= so119895 1198882= ho119895 1198883= ip119895 and 119888

4= tq119895 In general

so is the organism that expresses the peptide (but it caninclude also artificial peptides cellular lines etc) ho is thehost organism exposed to the peptide by means of the bpdetected with tq As our analysis based on the data reportedby IEDB we are unable to work with continuous values ofepitope activity 120576

119894119895 Consequently we have to predict the

discrete function of B-epitope efficiency 120582(120576119894119895) = 1 for epit-

opes reported in the conditions 119888119895and 120582(120576

119894119895) = 0 otherwise

Ourmain aim is to predict the shift or change in a function ofthe output efficiency Δ120582(120576

119894119895) = 120582(120576

119894119895)ref minus 120582(120576119894119895)new that takes

place after a change variation or perturbation (Δ119881) in thestructure andor boundary conditions of a peptide of refer-ence But we know the efficiency of the process of reference120582(120576119894119895)ref in addition to the molecular structure and the set of

conditions 119888119895for initial (reference) and final processes (new)

Consequently to predict Δ120582(120576119894119895) we have to predict only

120582(120576119894119895)new the efficiency function of the new state obtained by

a change in the structure of the peptide andor the boundaryconditions Let Δ119881 be a perturbation in a function 120582 we candefine 119881

119894119895as the state information function for the reference

and new states According to our recent model [58] we canwrite 119881

119894119895as a function of the conditions and structure of the

peptide 119898119894as follows In fact the variational state functions

119881119894119895have to be written in pairs in order to describe the initial

(reference) and final (new) states of a perturbation as follow

119881119894119895= 120582(120576

119894119895)new

minus

4

sum

119895=1

(120582 (119898119894) minus 120582(119888

119894119895)avg)

119881119902119903= 120582(120576

119902119903)refminus

4

sum

119903=1

(120582 (119898119902) minus 120582(119888

119902119903)avg)

(2)

The state function 119899119881119894119895is for the 119894th peptide measured

under a set of 119888119894119895boundary conditions in output final or new

stateThe conjugated state function 119903119881119902119903is for the 119902th peptide

measured under a set of 119888119902119903boundary conditions for the input

initial or reference state The difference Δ119881 between the new(output) state and the reference (input) state is the additiveperturbation [58] Consider

Δ119881 = 119881119894119895minus 119881119902119903= [

[

120582(120576119894119895)new

minus

4

sum

119895=1

(120582 (119898119894) minus 120582(119888

119894119895)avg)]

]

minus [120582(120576119902119903)refminus

4

sum

119903=1

(120582 (119898119902) minus 120582(119888

119902119903)avg)]

(3)

Equation (3) described before opens the door to testdifferent hypothesis A simple hypotheses is H

0 existence of

one small and constant value of the perturbation functionΔ119881 = 119890

0for all the pairs of peptides and a linear relationship

Table 1 Results of QSPR-perturbation model for IEDB B-Epitopes

Data Stat Pred Predicted epitope perturbationssubset param 120582(120576

119894119895) = 1 120582(120576

119894119895) = 0

120582(120576119894119895) = 1 Sp 970 84607 2660

120582(120576119894119895) = 0 Sn 936 4354 63548

Total train Ac 955120582(120576119894119895) = 1 Sp 971 28060 840

120582(120576119894119895) = 0 Sn 933 1485 20641

Total cv Ac 954Bold font is used to highlight the number of cases correctly classified by themodel

between perturbations of inputoutput boundary conditionswith coefficients 119886

119894119895 119887119894119895 119888119902119903 and 119889

119894119895 Consider

1198900= Δ119881

= [

[

119886119894119895sdot 120582(120576119894119895)new

minus

4

sum

119895=1

119887119894119895sdot (120582 (119898

119894) minus 120582(119888

119895)avg)]

]

minus [119888119902119903sdot 120582(120576119902119903)refminus

4

sum

119903=1

119889119902119903sdot (120582 (119898

119902) minus 120582(119888

119903)avg)]

(4)

We can use elemental algebraic operations to obtain fromthese equations an expression for efficiency as epitope of thepeptide 120582(120576

119894119895)new In this case considering 119887

119894119895asymp 119889119902119903 we can

obtain the different expressions the last may be very usefulto solve the QSRR problem for the large datasets formed byIEDB B-epitopes Consider

120582(120576119894119895)new

= (

119888119902119903

119886119894119895

) sdot 120582(120576119902119903)ref

+ [

[

4

sum

119895=1

(

119887119902119903

119886119894119895

) sdot (120582 (119898119894) minus 120582(119888

119895)avg)

new]

]

minus [

4

sum

119903=1

(

119889119902119903

119886119894119895

) sdot (120582 (119898119902) minus 120582(119888

119903)avg)ref

]

+ (

1198900

119886119894119895

)

120582(120576119894119895)new

=10158401198880sdot 120582(120576119902119903)ref

+

4

sum

119895=1

1015840119889119894119895sdot Δ (120582 (119898

119894) minus 120582(119888

119895)avg) +10158401198900

120582(120576119894119895)new

=10158401198880sdot 120582(120576119902119903)ref+

4

sum

119895=1

1015840119889119894119895sdot ΔΔ120582

119894119895119902119903+10158401198900

120582(120576119894119895)new

=10158401198880sdot 120582(120576119902119903)ref+

4

sum

119895=1

1015840119889119894119895sdot ΔΔ120594119894119895119902119903+10158401198900

(5)

4 Journal of Immunology Research

Table 2 Average values and count of input-output cases for differentorganisms process and techniques

Source organism (so) 119873in 119873outlowast120594

Homo sapiens 38920 39274 2685Plasmodium falciparum 10669 9446 2704Hepatitis C virus 9935 10239 2683Bos taurus 5671 5780 2690Canine parvovirus 5655 5637 2693Foot-mouth disease virus 4062 4176 2676Triticum aestivum 3769 3887 2703Bacillus anthracis 3602 3600 2699Human papillomavirus 3316 3414 2693Human herpesvirus 3026 3132 2684Gallus gallus 2850 2829 2689Arachis hypogaea 2648 2670 2687Mycobacterium tuberculosis 2637 2593 2688Clostridium botulinum 2588 2722 2685SARS coronavirus 2550 2704 2686Mus musculus 2334 2287 2682Hepatitis B virus 2007 2066 2680Helicobacter pylori 1958 1796 2695Hevea brasiliensis 1938 1958 2697Hepatitis E virus 1928 1941 2685Shigella flexneri 1878 1701 2699Dengue virus 2 1767 1828 2679Staphylococcus aureus 1757 1661 2694Treponema pallidum 1739 1755 2691Escherichia coli 1721 1678 2689Murine hepatitis virus 1575 1603 2692Haemophilus influenzae 1545 1587 2695Streptococcus mutans 1523 1537 2697Puumala virus (strain) 1505 1574 2689Chlamydia trachomatis 1402 1546 2704Human respiratory virus 1347 1398 2682Borrelia burgdorferi 1228 1237 2698Hepatitis delta virus 1182 1199 2690Streptococcus pyogenes 1181 1251 2697Porphyromonas gingivalis 1143 1085 2688Human enterovirus 1106 1132 2689Influenza A virus 1085 1086 2687Mycoplasma hyopneumoniae 1044 1024 2695Rattus norvegicus 1025 1039 2689Bordetella pertussis 1011 960 2685Human T-lymphotropic virus 996 1031 2680Anaplasma marginale 977 857 2707Measles virus strain 804 810 2688Fasciola hepatica 803 857 2685Neisseria meningitidis 789 853 2696Human poliovirus 766 780 2690Tityus serrulatus 764 775 2680Torpedo californica 752 788 2687

Table 2 Continued

Source organism (so) 119873in 119873outlowast120594

Cryptomeria japonica 719 794 2680Mycobacterium bovis 717 733 2688Trypanosoma cruzi 691 777 2704Andes virus CHI-7913 679 687 2690Bovine papillomavirus 672 665 2692Human hepatitis 670 696 2688Leishmania infantum 659 735 2688Human parvovirus 649 691 2683Poa pratensis 648 664 2692Aspergillus fumigatus 642 709 2677Duck hepatitis 587 603 2688Olea europaea 571 577 2692Porcine reproductive 515 514 2681Fagopyrum esculentum 509 497 2685Juniperus ashei 505 568 2672Mycobacterium leprae 489 542 2690Glycine max 477 509 2685D pteronyssinus 455 464 2680Plasmodium vivax 453 446 2690Chlamydophila pneumoniae 446 462 2690Pseudomonas aeruginosa 443 454 2691Vibrio cholera 427 426 2694Streptococcus sp 426 425 2691Mycobacterium avium 425 415 2689Dermatophagoides farinae 410 390 2693Human coxsackievirus 406 392 2694Equine infectious virus 404 419 2688Babesia equi 383 371 2696Prunus dulcis 383 379 2708Human adenovirus 375 405 2686Theileria parva 366 371 2713Candida albicans 365 370 2690Porcine endogenous 355 351 2692Ovis aries 352 350 2683Chironomus thummi 347 338 2691Sus scrofa 343 362 2686Bovine leukemia virus 333 329 2676Ricinus communis 329 314 2692Androctonus australis 322 357 2685Renibacterium salmoninarum 319 350 2690Orientia tsutsugamushi 309 372 2705Anacardium occidentale 293 306 2693Conus geographus 289 295 2660Host organism (ho) 119873in 119873out

lowast120594

Homo sapiens 257293 91093 26856Mus musculus 107867 51466 26873Oryctolagus cuniculus 65053 31433 26900Bos taurus 15333 2072 26909Rattus norvegicus 9450 3562 26876Aotus sp 9044 3933 26879Sus scrofa 7725 3464 26873Gallus gallus 7507 997 26790

Journal of Immunology Research 5

Table 2 Continued

Source organism (so) 119873in 119873outlowast120594

Canis lupus 6604 3334 26906Macaca mulatta 5261 2569 26993Ovis aries 3953 1653 26836Equus caballus 3943 2099 26842Cavia porcellus 3458 1688 26833Capra hircus 2182 1127 26830Aotus nancymaae 1659 852 26837Pan troglodytes 1614 732 26757Marmota monax 1100 509 27011Felis catus 901 279 26838Myodes glareolus 814 388 26863Anas platyrhynchos 688 342 26880Homo sapiens (human) 508 270 26851Trichosurus vulpecula 456 126 26921Mesocricetus auratus 438 104 26909Macaca cyclopis 382 193 26871O tshawytscha 333 159 26929Macaca fuscata 188 100 26667Cricetulus migratorius 171 142 27008Camelus dromedarius 171 89 26886Dicentrarchus labrax 121 55 26759Macaca fascicularis 96 52 26793Saimiri sciureus 92 44 26900Canis familiaris 77 42 26850Rattus rattus 72 31 26760Callithrix pygmaea 67 30 26920Chinchilla lanigera 41 24 26729Aotus lemurinus 30 19 26860Papio cynocephalus 27 13 27267Aotus griseimembra 26 12 27000Mustela vison 18 10 27000Chlorocebus aethiops 15 10 26875Bos indicus 13 4 26925Oncorhynchus mykiss 10 4 26700M macquariensis 9 6 26600Cricetulus griseus 8 4 26900Aotus trivirgatus 7 4 27000Process type (pt) 119873in 119873out

lowast120594

AID 111197 108536 26876OOID 32419 32617 26868OAID 19210 18954 26801OOA 15863 16303 26902NI 13430 15206 26845EWEIR 4818 4864 26843EEE 3113 3546 26906OOD 2806 2799 26887AICD 1077 1095 26812EWED 696 686 26879DEWED 280 337 26804TT 260 215 26806OOC 153 137 26800

Table 2 Continued

Source organism (so) 119873in 119873outlowast120594

Technique (tq) 119873in 119873outlowast120594

ELISA 133458 135109 26871WI 33627 33292 26887ACAbB 7780 9068 26862PhDIP 7450 4496 26771RIA 5241 5218 26858IFAIH 4454 4581 26879NIAA 4222 4316 26892FIA 2255 2276 26897PAC 1312 1219 26837IP 1127 1089 26886SPR 758 639 26860FACS 608 647 26907Other 502 495 26813SAC 484 393 26878ELISPOT 396 412 26979RDAT 366 323 26859EDAT 284 330 26800XRC 231 227 26880MS 209 179 26849PFF 171 153 26820AbDPO 162 295 26968CdC 146 205 26895IAbBA 144 183 26940IOT 124 106 26835HAGGI 115 122 26834IgMHR 89 90 26929EAAA 84 139 26922HS 82 67 26791AbdCC 73 118 26897AGG 50 60 26980CM 50 57 26863The lowastindicates that quantities like lowast120594 is the average value of the meanelectronegativity (119898119894) for all the peptides in IEDB that are epitopes for thesame boundary condition

3 Results and Discussion

We propose herein for the first time a QSRR-perturbationmodel able to predict variations in the propensity of a peptideto act as B-epitope taking into consideration the propensityof a peptide of reference and the changes in peptide sequenceimmunological process host organism source organismsand the experimental technique used The best QSPR-perturbation model found here with LDA was

120582(120576119894119895)new

= 4979 sdot 120582(120576119894119895)refminus 221642 sdot Δ120594seq

+ 8770 sdot ΔΔ120594ho + 63572 sdot ΔΔ120594so

minus 55387 sdot ΔΔ120594ip + 201919 sdot ΔΔ120594tq minus 2149

119873 = 155169 119877119888 = 092

119880 = 015 119901 lt 001

(6)

6 Journal of Immunology Research

Table3To

p100

values

ofp1

forp

ositive

perturbatio

nsin

training

serie

sNew

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

115153

MKG

VVC

TRIYEK

VHom

osapiens

Hom

osapiens

OAID

ELISA

115155

NNQRK

KAKN

TPFN

MLK

RERN

Mus

musculus

Denguev

irus2

AID

WI

001

0012

000

40017

0012

52124

QQQPP

Hom

osapiens

Triticum

aestivum

OOA

ELISA

52128

QQQQGG

SQSQ

KGKQ

QHom

osapiens

Glycinem

axOOA

WI

00minus0018

00002

3639

APL

GVT

Hom

osapiens

HepatitisE

virus

EWEIR

ELISA

3652

APL

TRG

SCRK

RNRS

PER

Hom

osapiens

Hum

anherpesvirus

OOID

ELISA

004

004

0039

0043

004

135959

LTRA

YAKD

VKF

GHom

osapiens

Hom

osapiens

OAID

ELISA

135963

NGQEE

KAG

VVS

TGLIGGG

Mus

musculus

MD

AID

ELISAminus004minus0038minus0047minus0033minus004

108075

PREP

QVY

Hom

osapiens

Hom

osapiens

OAID

ELISA

108078

PTSP

SGV

EEWIV

TQVVPG

VAOryctolagus

cuniculus

Hom

osapiens

AID

ACAb

Bminus001minus000

6minus001minus0003minus0011

25113

HVVDLP

Hom

osapiens

HepatitisE

virus

EWEIR

ELISA

25126

HWGNH

SKSH

PQR

Mus

musculus

MD

AID

ELISA

002

0022

0013

0023

002

48780

PPFSPQ

Hom

osapiens

Hepatitisd

elta

virus

OOID

ELISA

48782

PPFT

SAV

GGVDHRS

Mus

musculus

MD

AID

SAC

002

0022

0008

0021

0021

40988

LYVVA

YQA

Mus

musculus

Viscum

album

AID

ELISA

4100

4MAARL

CCQLD

PARD

VHom

osapiens

HepatitisB

virus

OOID

ELISA

002

0018

000

60019

002

50439

QDAY

NAAG

Mus

musculus

Mycobacteriu

mscrofulaceum

AID

ELISA

5044

5QDCN

CSI

YPGHAS

GHRM

AWD

Hom

osapiens

HepatitisC

virus

OOID

ELISA

004

0038

0028

0039

004

98849

KIPA

VFK

IDA

Hom

osapiens

Bostaurus

DEW

EDWI

98850

KKGSE

EEGDITNPIN

Hom

osapiens

Arachish

ypogaea

OOA

IFAIHminus005minus005minus0053minus004minus0051

116171

TQDQDP

BBHFF

KNIV

TPR

Hom

osapiens

Hom

osapiens

OAID

ACAb

B116

286

CGKG

LSAT

VTG

GQKG

RGSR

Oryctolagus

cuniculus

Mus

musculus

AID

MS

001

0014

0008

0017

000

9

123442

LLKD

LRKN

Hom

osapiens

Bornadiseasev

irus

EWEIR

WI

123443

LLTE

HR

MTW

DPA

QPP

RDLT

EHom

osapiens

Hom

osapiens

OOID

ELISAminus002minus002minus0025minus0017minus0022

47858

PHVVDL

Hom

osapiens

HepatitisE

virus

EWEIR

ELISA

47860

PHWIK

KPNRQ

GLG

YYS

Caprahircus

Hum

anT-lymphotropic

virus

AID

ACAb

B001

0007

0005

0013

000

9

61783

STNKA

VVS

LSBo

staurus

Bovine

respira

tory

AID

ELISA

61792

STNPK

PQRK

TKRN

TNRR

PQD

Hom

osapiens

HepatitisC

virus

OOID

ELISA

003

0025

0019

0029

003

118210

VMLY

QISEE

Hom

osapiens

Hom

osapiens

OAID

WI

118217

VTK

YITK

GWKE

VH

Oryctolagus

cuniculus

Hom

osapiens

AID

ELISA

005

0054

005

0057

004

8

130944

LFKH

SOryctolagus

cuniculus

Rattu

snorvegicus

AID

ELISA

130956

LPPR

VTP

KWSLDA

WST

WR

Hom

osapiens

MD

OOD

WI

001

000

6minus0002

0011

0012

Journal of Immunology Research 7Ta

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

23028

GVKY

AHom

osapiens

MD

OAID

WI

23032

GVLA

KDVRF

SQV

Hom

osapiens

MD

OOID

ELISA

00

00007minus0002

51199

QKK

AIE

Oryctolagus

cuniculus

Vibriocholerae

AID

ELISA

51204

QKK

NK

RNTN

RRPQ

DV

Hom

osapiens

HepatitisC

virus

OOID

ELISA

003

0026

0018

0029

003

144783

SHVVT

MLD

NF

Hom

osapiens

Hom

osapiens

NI

ELISA

144786

SMNRG

RGTH

PSLIWM

Mus

musculus

MD

AID

ACAb

B003

0032

0023

0033

0029

134343

DLY

IKMus

musculus

Hum

anpapillomaviru

sAID

NIAA

134344

DMAQ

VTV

GPG

LLGVS

TLMus

musculus

Hom

osapiens

AID

WI

00minus000

90

0

38321

LNQLA

GRM

Anas

platyrhynchos

Duckhepatitis

AID

ELISA

38323

LNQTA

RAFP

DCA

ICW

EPSP

POryctolagus

cuniculus

Bovine

leukemia

virus

AID

ACAb

Bminus001minus0008minus0022minus001minus0011

144657

GQITVD

MMYG

Hom

osapiens

Hom

osapiens

OAID

ELISA

1446

61GRE

GYP

ADGGCA

WPA

CYC

Oryctolagus

cuniculus

MD

AID

WI

002

0024

0013

0027

0022

21084

GLQ

NMus

musculus

Chlamydia

trachom

atis

AID

ELISA

21093

GLR

AQD

DFS

GWDI

NTP

AFE

WMus

musculus

Mycobacteriu

mtuberculosis

AID

WI

003

003

0013

003

0032

98453

SGFS

GSV

QFV

Oryctolagus

cuniculus

Neisseria

meningitid

isAID

ELISA

98456

SICS

NN

PTCW

AIC

KRIPNKK

Mus

musculus

Hum

anrespira

tory

virus

AID

IFAIH

004

0037

0027

004

004

1

98453

SGFS

GSV

QFV

Mus

musculus

Neisseria

meningitid

isAID

ELISA

98456

SICS

NN

PTCW

AIC

KRIPNKK

Mus

musculus

Hum

anrespira

tory

virus

AID

IFAIH

004

004

0027

004

004

1

107107

EAIQ

PRa

ttus

norvegicu

sHom

osapiens

AID

ELISA

107110

EKER

RPSP

IGTA

TLL

Hom

osapiens

MD

OOA

ELISA

005

004

80043

0053

005

110857

FTGEA

YSY

WSA

KHom

osapiens

Mycoplasm

apenetra

nsEW

EIR

ELISA

110859

GEE

SRIS

LPLP

NF

SSLN

LRE

Mus

musculus

Hom

osapiens

AID

FIA

00002minus0017

0003

0003

36315

LGSA

YPMus

musculus

Mycobacteriu

mlep

rae

MD

ELISA

36317

LGSG

AFG

TIYK

GMus

musculus

Avian

erythroblasto

sisvirus

AID

ACAb

B001

001

00011

000

9

122034

WNPA

DRa

ttus

norvegicu

sTorpedocalifornica

AID

ELISA

122035

WNPA

DYG

GIK

WN

PADYG

GIK

Rattu

snorvegicu

sMD

AID

RIA

001

001

0001

001

000

9

25013

HVA

DID

KLID

Mus

musculus

Puum

alavirus

Kazan

AID

ELISA

25021

HVA

PTH

YVTE

SDA

SQRV

TQL

Hom

osapiens

HepatitisC

virus

OOID

ELISA

0minus0002minus0014minus0001

0

36162

LGIH

EOryctolagus

cuniculus

Cand

idaalbicans

AID

ELISA

36166

LGIM

GE

YRGTP

RNQDLY

DAA

Mus

musculus

Hum

anrespira

tory

virus

AID

RIA

0minus0003minus0007

0minus0001

67253

TWEV

LHMus

musculus

Plasmodium

vivax

AID

ELISA

67257

TWGEN

ETDVLL

LNNTR

PPQ

Hom

osapiens

HepatitisC

virus

OOID

ACAb

Bminus002minus0022minus0027minus0021minus0021

8 Journal of Immunology Research

Table3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

50990

QGYR

VSSY

LPHom

osapiens

Hevea

brasiliensis

OOA

WI

50998

QHEQ

DR

PTPS

PAP

SRPF

SVL

Hom

osapiens

HepatitisE

virus

OOID

ELISA

001

001minus0002

0007

0008

100458

RDVLQ

LYAPE

Mus

musculus

Bacillusa

nthracis

AID

ELISA

1004

62RF

STRY

GNQNGRI

RVLQ

RFD

Hom

osapiens

Arachish

ypogaea

EWED

ELISA

003

0028

0018

003

003

111036

TEST

FTGEA

YSV

Hom

osapiens

Mycoplasm

apenetra

nsEW

EIR

ELISA

111039

TGVPID

PAVPD

SSIV

PLLE

SBo

staurus

Bovine

papillomaviru

sAID

ELISA

003

0035

002

0033

003

117919

IFIEME

Hom

osapiens

Hom

osapiens

OAID

WI

117921

IGIIDLIE

KRKF

NQ

Mus

musculus

Hom

osapiens

AID

WI

003

0032

003

0037

003

7127

CTDTD

KLF

Oryctolagus

cuniculus

Shigellaflexneri

AID

ELISA

7128

CTDVS

TAIH

ADQL

TPAW

Hom

osapiens

SARS

coronavirus

OOID

ELISA

001

000

6minus0003

000

9001

112253

PGQSP

KLHom

osapiens

Hom

osapiens

OAID

ELISA

112255

PIRA

LVGDEV

ELP

CRISPG

KMus

musculus

Hom

osapiens

AID

ELISA

001

0012

001

0017

001

122034

WNPA

DRa

ttus

norvegicu

sTorpedocalifornica

AID

ELISA

122038

WNPD

DY

GGVKW

NP

DDYG

GVK

Rattu

snorvegicu

sMD

AID

RIA

00minus000

90minus0001

131878

FLMLV

GGST

LHom

osapiens

Hom

osapiens

OAID

ACAb

B131879

FLVA

HT

RARA

PSA

GER

ARR

SMus

musculus

Mus

musculus

AID

NIAA

003

0032

0028

0037

0033

7066

4VQ

VVYD

YQHom

osapiens

Treponem

apallidu

mOOID

ELISA

70667

VQWMNR

LIAFA

FAG

NHVS

PHom

osapiens

HepatitisC

virus

OOID

ELISA

005

005

004

1005

005

71545

VTV

Hom

osapiens

Helicobacterpylori

OOID

ELISA

71559

VTV

RGGL

RILSPD

RKHom

osapiens

Arachish

ypogaea

OOA

WI

004

004

0032

0043

004

2

127856

TDVRY

KD

Mus

musculus

Mus

musculus

AID

ACAb

B127857

TDVRY

KDDMYH

FFCP

AIQ

AQMus

musculus

Mus

musculus

AID

PFF

001

001

001

001

000

6

112149

GVG

WIRQ

Hom

osapiens

Hom

osapiens

OAID

ELISA

112152

HHPA

RTAHYG

SLP

QKS

HGRT

Hom

osapiens

Hom

osapiens

AID

ELISA

00

00007

0

119581

FSCS

VMHE

Hom

osapiens

Hom

osapiens

OAID

ELISA

119582

GLQ

LIQL

INVDEV

NQI

Mus

musculus

Hom

osapiens

AID

RIAminus001minus0008minus001minus0003minus0011

144657

GQITVD

MMYG

Hom

osapiens

Hom

osapiens

OAID

ELISA

144659

GRE

GYP

ADGGAA

GYC

NTE

Oryctolagus

cuniculus

MD

AID

WIminus001minus000

6minus0017minus0003minus0008

25013

HVA

DI

DKL

IDMus

musculus

Puum

alavirus

Kazan

AID

ELISA

25022

HVA

PTH

YVVES

DA

SQRV

TQV

Hom

osapiens

HepatitisC

virus

OOID

ELISA

0minus0002minus0014minus0001

0

Journal of Immunology Research 9Ta

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

144652

GMRG

MKG

LVY

Hom

osapiens

Hom

osapiens

OAID

ELISA

144654

GPH

PTLE

VVPM

GRG

SMus

musculus

MD

AID

ELISAminus002minus0018minus0027minus0013minus002

104515

HDCR

PKKI

Mus

musculus

LaCrossevirus

AID

IFAIH

104521

IGTL

KKIL

DET

VKD

KIAKE

QRa

ttus

norvegicu

sStreptococcus

pyogenes

AID

ELISAminus004minus004minus0053minus004minus004

1

7367

CYGDWA

Hom

osapiens

Triticum

aestivum

OOA

ELISA

7374

CYGLP

DS

EPTK

TNGK

Mus

musculus

Tityus

serrulatus

AID

WIminus002minus0018minus0043minus0023minus0018

7367

CYGDWA

Hom

osapiens

Triticum

aestivum

OOA

ELISA

7374

CYGLP

DS

EPTK

TNGK

Mus

musculus

Tityus

serrulatus

AID

WIminus002minus0018minus0043minus0023minus0018

144610

DFF

TYK

Mus

musculus

Porcine

transmissible

AID

WI

144611

DFN

GSF

DMNGTITA

Oryctolagus

cuniculus

Escherich

iacoli

AID

ELISAminus001minus0007minus0018minus001minus0012

112047

AST

RESG

Hom

osapiens

Hom

osapiens

OAID

ELISA

112048

ATAST

MDHARH

GF

LPRH

RDT

Hom

osapiens

Hom

osapiens

AID

ELISA

005

005

005

0057

005

36136

LGGVFT

Hom

osapiens

Denguev

irus2

OOID

ELISA

36137

LGGWKL

QSD

PRAY

AL

Hom

osapiens

Ambrosia

artemisiifolia

OOA

RIA

001

001

0007

0013

000

9

115256

FREL

KDLK

GY

Hom

osapiens

Bostaurus

DEW

EDWI

115261

GDLE

ILL

QKW

ENG

ECAQ

KKI

Hom

osapiens

Bostaurus

OOA

FIAminus001minus001minus001

0minus000

9

129024

KADQLY

KHom

osapiens

Hom

osapiens

OAID

ELISA

129026

KAKK

PAAAAG

AKK

AKS

Oryctolagus

cuniculus

Hom

osapiens

AID

ELISA

003

0034

003

0037

003

148481

YTRD

LVYK

Rattu

snorvegicu

sHom

osapiens

AID

WI

148483

YVPIVT

FYSE

ISM

HSSRA

IPOryctolagus

cuniculus

MD

AID

ELISA

00002minus0007

0minus0002

150850

GY

Mus

musculus

Hum

anpapillomaviru

sAID

ELISA

150853

HIG

GLSI

LDPIFG

VL

Hom

osapiens

Dermatophagoides

farin

aeOOA

ACAb

B004

0038

004

0043

0039

107366

FPPK

PKD

Hom

osapiens

Hom

osapiens

OAID

ELISA

107376

GDRS

GYS

SPGSP

GMus

musculus

Hom

osapiens

AID

ACAb

Bminus003minus0028minus003minus0023minus0031

114859

ICGTD

GVTY

THom

osapiens

Gallusgallus

OOA

WI

114865

IVER

ETR

GQSE

NPL

WHALR

RRa

ttus

norvegicu

sHum

anherpesvirus

AID

ELISA

004

004

20035

0037

0038

62149

SVHLF

Hom

osapiens

MD

OAID

WI

62150

SVIALG

SQEG

ALH

QALA

GAI

Equu

scaballus

WestN

ilevirus

AID

IFAIHminus002minus0021minus0012minus0013minus0021

98455

SGSV

QFV

PIQ

Mus

musculus

Neisseria

meningitid

isAID

ELISA

98456

SICS

NNP

TCWAICK

RIPN

KKMus

musculus

Hum

anrespira

tory

virus

AID

IFAIH

004

004

0027

004

004

1

61783

STNKA

VVS

LSBo

staurus

Bovine

respira

tory

AID

ELISA

61791

STNPK

PQRK

TKRN

TNRR

PQHom

osapiens

HepatitisC

virus

EWEIR

ACAb

B004

0035

0029

0037

0039

100318

NAPK

TFQ

FIN

Mus

musculus

Bacillusa

nthracis

AID

ELISA

100319

NASSEL

HLL

GFG

INAEN

NHR

Hom

osapiens

Arachish

ypogaea

EWED

ELISA

0minus0002minus0012

00

10 Journal of Immunology ResearchTa

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

118947

PFSA

PPPA

Hom

osapiens

Hom

osapiens

OAID

ELISA

118948

PGAIEQG

PADDPG

EGPS

TGP

Hom

osapiens

Hum

anherpesvirus

NI

ACAb

Bminus004minus004minus004

1minus0036minus004

1

78323

YSFR

DMus

musculus

Bluetongue

virus1

AID

ELISA

78341

AALT

AEN

TAIK

KRN

ADAKA

Hom

osapiens

Streptococcus

mutan

sEE

EEL

ISA

001

0008

0007

0013

001

145831

IPLG

TRP

Mus

musculus

Hum

anpapillomaviru

sAID

ELISA

145841

KEDFR

YAISST

NEI

GLL

GA

Susscrofa

Classicalsw

ine

AID

PACminus004minus004minus0045minus004minus0043

119592

HTF

PAVLQ

Hom

osapiens

Hom

osapiens

OAID

ELISA

119596

IHIPSE

KIWRP

DLV

LYMus

musculus

Hom

osapiens

AID

RIA

001

0012

001

0017

000

9

58780

SKAANLSIIK

MD

Beetnecrotic

MD

WI

58783

SKAFSN

CYPY

DVP

DYA

SLOryctolagus

cuniculus

Influ

enza

Avirus

AID

RIAminus001minus000

4minus0014minus000

9minus0013

115153

MKG

VVC

TRIYEK

VHom

osapiens

Hom

osapiens

NI

ELISA

115155

NNQRK

KAKN

TPFN

MLK

RERN

Mus

musculus

Denguev

irus2

AID

ELISA

001

0012

000

40013

001

133629

LPLR

FOryctolagus

cuniculus

Gallusgallus

AID

ACAb

B133630

LPPG

LHV

FPLA

SNRS

Mus

musculus

MD

AID

SPRminus001minus0013minus0021minus001minus001

96215

EEEE

AE

DKE

DHom

osapiens

Hom

osapiens

OAID

ELISA

96216

EEEG

LLK

KSADTL

WNMQK

Mus

musculus

Mus

musculus

AID

ELISA

008

0082

0078

0087

008

39782

LTAASV

Hom

osapiens

Triticum

aestivum

OOA

ELISA

39788

LTAEL

KIYS

VIQ

AEI

NKH

LOryctolagus

cuniculus

Yersiniapestis

AID

ACAb

B001

0014minus0001

0007

000

9

107479

KFNWYV

DHom

osapiens

Hom

osapiens

OAID

ELISA

107482

KGEP

GL

PGRG

FPGFP

Mus

musculus

Hom

osapiens

AID

ACAb

B0

0002

00007minus0001

63569

TETV

NSD

IMacaca

mulatta

Shigellaflexneri

AID

ELISA

63573

TEVEL

KER

KHRIED

AVRN

AK

Hom

osapiens

Mycobacteriu

mlep

rae

OOID

ELISA

005

0036

004

10049

005

134028

DDTIS

Mus

musculus

Hom

osapiens

AID

NIAA

134029

DED

ENQS

PRSFQKK

TROryctolagus

cuniculus

Hom

osapiens

AID

ELISA

005

0053

005

005

004

8

23028

GVKY

AHom

osapiens

MD

OAID

WI

23032

GVLA

KDV

RFSQ

VHom

osapiens

MD

EWEIR

ACAb

B0

00

000

4minus0003

115293

IMCV

KKILDK

Hom

osapiens

Bostaurus

DEW

EDWI

115295

INPS

KEN

LCST

FCK

EVVRN

AHom

osapiens

Bostaurus

OOA

FIAminus003minus003minus003minus002minus0029

65105

TLTP

ENTL

Mus

musculus

Shigellaflexneri

AID

ELISA

65110

TLTS

GSD

LDRC

TTFD

DV

Oryctolagus

cuniculus

SARS

coronavirus

AID

ELISA

001

0013minus0003

001

001

1344

71PK

PEQ

Mus

musculus

Streptococcus

pneumoniae

AID

FACS

1344

72PL

LPGT

STTS

TGPC

KTHom

osapiens

HepatitisB

virus

AID

ELISA

002

0018

0019

002

0016

Journal of Immunology Research 11Ta

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

142228

LYCY

EQLN

DSSE

Hom

osapiens

Hum

anpapillomaviru

sNI

ELISA

142250

NWGDEP

SKRR

DRS

NSR

GRK

NFelis

catus

Felin

einfectio

usAID

ELISA

007

006

80071

0073

007

21549

GNYD

FWYQ

SHom

osapiens

Staphylococcus

aureus

OOID

ELISA

21553

GNYN

YKY

RYLR

HGK

LRPF

ERMus

musculus

SARS

coronavirus

AID

ELISA

003

0032

0021

0031

003

21549

GNYD

FWYQ

SHom

osapiens

Staphylococcus

aureus

OOID

ELISA

21553

GNYN

YKY

RYLR

HGK

LRPF

EROryctolagus

cuniculus

SARS

coronavirus

AID

ELISA

003

0034

0021

0031

003

34908

LAPL

GE

Hom

osapiens

HepatitisE

virus

EWEIR

ELISA

34914

LAPS

TLRS

LRKR

RLSSP

Hom

osapiens

Hum

anherpesvirus

OOID

ELISA

006

006

0059

0063

006

130454

CLFP

NNSY

CMus

musculus

MD

AID

NIAA

130456

CRPQ

VN

NPK

EWS

CAAC

Hom

osapiens

MD

OOD

ACAb

B001

0008

001

0011

0007

1964

4GFV

PSM

Hom

osapiens

Hepatitisd

elta

virus

OOID

ELISA

1964

7GFV

SASI

FGFQ

AEV

GPN

NTR

Oryctolagus

cuniculus

VacciniavirusW

RAID

ELISAminus001minus000

6minus0013minus000

9minus001

20678

GKRP

EMus

musculus

Streptococcus

pyogenes

AID

WI

20680

GKS

KRD

AKN

NAAK

LAVDKL

LMus

musculus

VacciniavirusW

RAID

WI

001

001

0001

001

001

123278

GYL

KDLP

TTOvisa

ries

Fasciolahepatica

AID

ELISA

123282

HAC

QKK

LLKF

EAL

QQEE

GEE

Rattu

snorvegicu

sGallusgallus

AID

PFFminus002minus0016minus0016minus002minus0025

141067

PLSLEP

DP

Mus

musculus

Hom

osapiens

AID

FACS

141073

REGVRW

RVMAIQ

Mus

musculus

Hom

osapiens

AID

ELISA

006

006

006

006

0056

139248

SFAG

TVIE

Mus

musculus

Classicalsw

ine

AID

WI

139305

TAAQ

ITQ

RKWEA

ARE

AEQ

RROryctolagus

cuniculus

Hom

osapiens

AID

IP003

0033

0026

003

003

104515

HDCR

PKKI

Mus

musculus

LaCrossevirus

AID

IFAIH

104520

IAKE

QE

NKE

TIGT

LKKI

LDE

Rattu

snorvegicu

sStreptococcus

pyogenes

AID

ELISAminus006minus006minus0073minus006minus0061

113517

HLY

ADGLT

DMus

musculus

Hum

anpapillomaviru

sAID

IFAIH

113518

HNKI

QA

IELE

DLL

RYS

KLYR

Mus

musculus

Hom

osapiens

AID

ELISA

002

002

0011

002

0019

156970

VER

HQ

Hom

osapiens

Hom

osapiens

OAID

ELISA

156975

WSSTV

LRVS

PTRT

VP

Mus

musculus

MD

AID

ELISA

001

0012

0003

0017

001

78252

PVQNLT

Mus

musculus

Porphyromonas

gingivalis

AID

WI

78253

QGGCG

RGWAFS

ATG

AIEA

Mus

musculus

Glycinem

axAID

ELISA

002

002

0017

002

0018

6068

CCPD

KNKS

Mus

musculus

Hum

anherpesvirus

AID

WI

6074

CCRH

KQKD

VGDVK

QTL

PPS

Ovisa

ries

MD

AID

ELISA

001

000

6000

4001

0008

12 Journal of Immunology Research

Table3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

53109

RAGVC

YHom

osapiens

Triticum

aestivum

OOA

ELISA

53116

RAILTA

FSPA

QDI

WGTS

Oryctolagus

cuniculus

SARS

coronavirus

AID

ELISAminus002minus0016minus0037minus0023minus002

147041

IPEQ

Hom

osapiens

Triticum

aestivum

OOA

WI

147064

KHQGA

QYV

WN

RTA

Hom

osapiens

Bostaurus

OOID

WI

006

006

0047

0057

006

7066

4VQ

VVYD

YQOryctolagus

cuniculus

Treponem

apallidu

mAID

ELISA

70667

VQWMN

RLIAFA

FAG

NHVS

PHom

osapiens

HepatitisC

virus

OOID

ELISA

005

004

6004

10049

005

134343

DLY

IKMus

musculus

Hum

anpapillomaviru

sAID

PhDIP

134344

DMAQ

VTV

GPG

LLGVS

TLMus

musculus

Hom

osapiens

AID

PhDIP

00minus000

90

0

Journal of Immunology Research 13

The first input term is the value 120582(120576119894119895)ref is the scoring

function 120582 of the efficiency of the initial process 120576119894119895(known

solution) The function 120582(120576119894119895)ref = 1 if the 119894th peptide could

experimentally be demonstrated to be a B-epitope in theassay of reference (reference) carried out in the conditions119888119895 120582(120576119894119895)ref = 0 otherwise The variational-perturbation

terms ΔΔ120594119888119895are at the same time terms typical of pertur-

bation theory and moving average (MA) functions used inBox-Jenkin models in time series [60] These new types ofterms account both for the deviation of the electronegativityof all amino acids in the sequence of the new peptidewith respect to the peptide of reference and with respectto all boundary conditions In Table 1 we give the overallclassification results obtainedwith thismodel Speck-Plancheet al [61ndash63] introduced different multitargetmultiplexingQSARmodels that incorporate this type of information basedon MAs The results obtained with the present model areexcellent comparedwith other similarmodels in the literatureuseful for other problems including moving average models[64 65] or perturbation models [58] Notably this is also thefirst model combining both perturbation theory and MAs ina QSPR context

The other input terms are the following The first Δ120594seq =120594(119898119902)ref minus120594(119898119894)new is the perturbation term for the variation

or in themean value of electronegativity for all amino acids inthe sequence of the peptide of reference The remnant inputvariables of the model ΔΔ120594

119888119895= Δ120594

119888119895-ref minus Δ120594119888119895-new =

[120594(119898119902)ref minus

lowast120594 (119888119902119903)ref] minus [120594(119898119894)new minus

lowast120594 (119888119894119895)new] quantify

values of the conditions of the new assay 119888119895-new that representperturbations with respect to the initial conditions 119888

119894119895-ref of

the assay of reference The quantities lowast120594(119888119894119895) and lowast120594(119888

119902119903) are

the average values of the mean electronegativity values 120594(119898119894)

and 120594(119898119902) for all new and reference peptides in IEDB that

are epitopes under the 119895th or 119903th boundary condition Thevalues of these terms have been tabulated for gt500 sourceorganisms gt50 host organisms gt10 biological process andgt30 experimental techniques We must substitute the valuesof 120594(119898

119894) and 120594(119898

119902) of the new and reference peptides and the

tabulated values of lowast120594(119888119894119895) and lowast120594(119888

119902119903) for all combinations

of boundary conditions to predict the perturbations of theaction as epitope of peptides In doing so we can found theoptimal sequence and boundary conditions towards the useof the peptide in the development of a vaccine In Table 2 wegive some of these values of lowast120594(119888

119894119895) and lowast120594(119888

119902119903)

In Table 3 we depict the sequences and input-outputboundary conditions for top perturbations present in IEDBAll these perturbations have observed value of 120582(120576

119894119895)new = 1

and predicted value also equal to 1 with a high probabilitySee Supplementary Material available online at httpdxdoiorg1011552014768515 file contains a full list of gt200000cases of perturbations

4 Conclusions

It is possible to develop generalmodels for vaccine design ableto predict the results of multiple input-output perturbations

in peptide sequence and experimental assay boundary condi-tions using ideas of QSPR analysis perturbation theory andBox and Jenkins MA operators The electronegativity valuescalculated withMARCH-INSIDE seem to be goodmoleculardescriptors for this type of QSPR-perturbation models

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

The present study was partially supported by GrantsAGL2010-22290-C02 and AGL2011-30563-C03 from Minis-terio de Ciencia e Innovacion Spain and Grant CN 2012155from Xunta de Galicia Spain

References

[1] B Peters J Sidney P Bourne et al ldquoThe design and implemen-tation of the immune epitope database and analysis resourcerdquoImmunogenetics vol 57 no 5 pp 326ndash336 2005

[2] B Peters J Sidney P Bourne et al ldquoThe immune epitopedatabase and analysis resource from vision to blueprintrdquo PLoSBiology vol 3 no 3 p e91 2005

[3] PWang A AMorgan Q Zhang A Sette and B Peters ldquoAuto-mating document classification for the Immune Epitope Data-baserdquo BMC Bioinformatics vol 8 article 269 2007

[4] A Sette ldquoThe immune epitope database and analysis resourcefrom vision to blueprintrdquo Genome Informatics vol 15 no 2 p299 2004

[5] N Salimi W Fleri B Peters and A Sette ldquoThe immune epi-tope database a historical retrospective of the first decaderdquoImmunology vol 137 no 2 pp 117ndash123 2012

[6] Y Kim A Sette and B Peters ldquoApplications for T-cell epitopequeries and tools in the Immune EpitopeDatabase andAnalysisResourcerdquo Journal of Immunological Methods vol 374 no 1-2pp 62ndash69 2011

[7] Y Kim J Ponomarenko Z Zhu D Tamang et al ldquoImmuneepitope database analysis resourcerdquo Nucleic Acids Research ppW525ndashW530 2012

[8] A M Helguera R D Combes M P Gonzalez and M N DS Cordeiro ldquoApplications of 2D descriptors in drug design aDRAGON talerdquo Current Topics in Medicinal Chemistry vol 8no 18 pp 1628ndash1655 2008

[9] GM Casanola-Martın Y Marrero-Ponce M T H Khan et alldquoDragon method for finding novel tyrosinase inhibitors Biosil-ico identification and experimental in vitro assaysrdquo The Euro-pean Journal of Medicinal Chemistry vol 42 no 11-12 pp 1370ndash1381 2007

[10] I V Tetko J Gasteiger R Todeschini et al ldquoVirtual computa-tional chemistry laboratorymdashdesign and descriptionrdquo Journalof Computer-AidedMolecular Design vol 19 no 6 pp 453ndash4632005

[11] A R Katritzky A Oliferenko A Lomaka and M KarelsonldquoSix-membered cyclic ureas as HIV-1 protease inhibitors aQSAR study based on CODESSA PRO approachrdquo Bioorganicand Medicinal Chemistry Letters vol 12 no 23 pp 3453ndash34572002

14 Journal of Immunology Research

[12] A R Katritzky S Perumal R Petrukhin and E Klein-peter ldquoCODESSA-based theoretical QSPRmodel for hydantoinHPLC-RT lipophilicitiesrdquo Journal of Chemical Information andComputer Sciences vol 41 no 3 pp 569ndash574 2001

[13] S Vilar G Cozza and S Moro ldquoMedicinal chemistry and themolecular operating environment (MOE) application of QSARand molecular docking to drug discoveryrdquo Current Topics inMedicinal Chemistry vol 8 no 18 pp 1555ndash1572 2008

[14] G Marzaro A Chilin A Guiotto et al ldquoUsing the TOPS-MODE approach to fit multi-target QSAR models for tyrosinekinases inhibitorsrdquo The European Journal of Medicinal Chem-istry vol 46 no 6 pp 2185ndash2192 2011

[15] S Vilar E Estrada E Uriarte L Santana and Y Gutierrez ldquoInsilico studies toward the discovery of new anti-HIV nucleosidecompounds through the use of tops-mode and 2D3D con-nectivity indices 2 Purine derivativesrdquo Journal of ChemicalInformation and Modeling vol 45 no 2 pp 502ndash514 2005

[16] E Estrada J A Quincoces and G Patlewicz ldquoCreating molec-ular diversity from antioxidants in Brazilian propolis combina-tion of TOPS-MODE QSAR and virtual structure generationrdquoMolecular Diversity vol 8 no 1 pp 21ndash33 2004

[17] E Estrada andHGonzalez ldquoWhat are the limits of applicabilityfor graph theoretic descriptors in QSPRQSAR modelingdipole moments of aromatic compounds with TOPS-MODEdescriptorsrdquo Journal of Chemical Information and ComputerSciences vol 43 no 1 pp 75ndash84 2003

[18] Y Marrero-Ponce J A Castillo-Garit E Olazabal et al ldquoTom-ocomd-Cardd a novel approach for computer-aided ldquorationalrdquodrug design I Theoretical and experimental assessment of apromising method for computational screening and in silicodesign of new anthelmintic compoundsrdquo Journal of Computer-Aided Molecular Design vol 18 no 10 pp 615ndash634 2004

[19] Y Marrero Ponce R Medina Marrero E A Castro et al ldquoPro-tein quadratic indices of the ldquomacromolecular pseudographrsquos 120572-carbon atom adjacency matrixrdquo 1 prediction of arc repressoralanine-mutantrsquos stabilityrdquoMolecules vol 9 no 12 pp 1124ndash11472004

[20] H Gonzalez-Dıaz F Prado-Prado and F M Ubeira ldquoPredict-ing antimicrobial drugs and targets with the MARCH-INSIDEapproachrdquo Current Topics in Medicinal Chemistry vol 8 no 18pp 1676ndash1690 2008

[21] R Garcıa-Domenech J Galvez J V de Julian-Ortiz and LPogliani ldquoSomenew trends in chemical graph theoryrdquoChemicalReviews vol 108 no 3 pp 1127ndash1169 2008

[22] E Estrada and E Uriarte ldquoRecent advances on the role of topo-logical indices in drug discovery researchrdquo Current MedicinalChemistry vol 8 no 13 pp 1573ndash1588 2001

[23] R Todeschini andVConsonniHandbook ofMolecularDescrip-tors Wiley-VCH Weinheim Germany 2008

[24] E Estrada E J Delgado J B Alderete and G A Jana ldquoQuan-tum-connectivity descriptors inmodeling solubility of environ-mentally important organic compoundsrdquo Journal of Computa-tional Chemistry vol 25 no 14 pp 1787ndash1796 2004

[25] E Besalu X Girones L Amat andR Carbo-Dorca ldquoMolecularquantum similarity and the fundamentals of QSARrdquo Accountsof Chemical Research vol 35 no 5 pp 289ndash295 2002

[26] D A Rincon M N D S Cordeiro and R A Mosquera ldquoOnthe electronic structure of cocaine and its metabolitesrdquo Journalof Physical Chemistry A vol 113 no 50 pp 13937ndash13942 2009

[27] M Mandado M J Gonzalez-Moa and R A MosqueraldquoChemical graph theory and n-center electron delocalization

indices a study on polycyclic aromatic hydrocarbonsrdquo Journalof Computational Chemistry vol 28 no 10 pp 1625ndash1633 2007

[28] THill andP Lewicki STATISTICSMethods andApplications AComprehensive Reference for Science Industry and DataMiningStatSoft Tulsa Okla USA 2006

[29] E Frank M Hall L Trigg G Holmes and I H Witten ldquoDatamining in bioinformatics using Wekardquo Bioinformatics vol 20no 15 pp 2479ndash2481 2004

[30] G Patlewicz N Ball E D Booth E Hulzebos E Zvinavasheand C Hennes ldquoUse of category approaches read-across and(Q)SAR general considerationsrdquo Regulatory Toxicology andPharmacology vol 67 no 1 pp 1ndash12 2013

[31] D W Roberts and G Y Patlewicz ldquoUpdating the skin sensiti-zation in vitro data assessment paradigm in 2009mdasha chemistryand QSAR perspectiverdquo Journal of Applied Toxicology vol 30no 3 pp 286ndash288 2010

[32] D W Roberts and G Y Patlewicz ldquoNonanimal alternatives forskin sensitization letter to the editorrdquoToxicological Sciences vol106 no 2 pp 572ndash574 2008

[33] E Estrada G Patlewicz and Y Gutierrez ldquoFrom knowledgegeneration to knowledge archive a general strategy usingTOPS-MODE with DEREK to formulate new alerts for skinsensitizationrdquo Journal of Chemical Information and ComputerSciences vol 44 no 2 pp 688ndash698 2004

[34] G F Gerberick C A Ryan P S Kern et al ldquoA chemical datasetfor evaluation of alternative approaches to skin-sensitizationtestingrdquo Contact Dermatitis vol 50 no 5 pp 274ndash288 2004

[35] G Y Patlewicz D A Basketter C K Smith Pease et al ldquoFurtherevaluation of quantitative structurendashactivity relationship mod-els for the prediction of the skin sensitization potency ofselected fragrance allergensrdquo Contact Dermatitis vol 50 no 2pp 91ndash97 2004

[36] E Estrada G Patlewicz M Chamberlain D Basketter andS Larbey ldquoComputer-aided knowledge generation for under-standing skin sensitization mechanisms the TOPS-MODEapproachrdquo Chemical Research in Toxicology vol 16 no 10 pp1226ndash1235 2003

[37] G Patlewicz R Rodford and J D Walker ldquoQuantitative struc-ture-activity relationships for predicting skin and eye irritationrdquoEnvironmental Toxicology and Chemistry vol 22 no 8 pp1862ndash1869 2003

[38] G Y Patlewicz R A Rodford G Ellis and M D Barratt ldquoAQSAR model for the eye irritation of cationic surfactantsrdquo Tox-icology In Vitro vol 14 no 1 pp 79ndash84 2000

[39] E Tenorio-Borroto C G Penuelas Rivas J C VasquezChagoyan N Castanedo F J Prado-Prado and X Garcia-Mera ldquoANN multiplexing model of drugs effect on macro-phages theoretical and flow cytometry study on the cyto-toxicity of the anti-microbial drug G1 in spleenrdquo Bioorganic ampMedicinal Chemistry vol 20 no 20 pp 6181ndash6194 2012

[40] D R Flower H McSparron M J Blythe et al ldquoComputationalvaccinology quantitative approachesrdquo Novartis FoundationSymposium vol 254 pp 102ndash120 2003

[41] I A Doytchinova P Guan and D R Flower ldquoQuantitativestructure-activity relationships and the prediction of MHCsupermotifsrdquoMethods vol 34 no 4 pp 444ndash453 2004

[42] Y Xiao and M R Segal ldquoPrediction of genomewide conservedepitope profiles of HIV-1 classifier choice and peptide rep-resentationrdquo Statistical Applications in Genetics and MolecularBiology vol 4 no 1 article 25 2005

Journal of Immunology Research 15

[43] T Fagerberg V Zoete S Viatte P Baumgaertner P M Alves PRomero et al ldquoPrediction of cross-recognition of peptide-HLAA2 by melan-a-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationshipsrdquo PLoSONE vol 8 no 7 Article ID e65590 2013

[44] D Barh A N Misra A Kumar and A Vasco ldquoA novel strategyof epitope design inNeisseria gonorrhoeaerdquo Bioinformation vol5 no 2 pp 77ndash82 2010

[45] J Bi R Song H Yang et al ldquoStepwise identification of HLA-Alowast0201-restricted CD8+ T-cell epitope peptides from herpessimplex virus type 1 genome boosted by a steprank schemerdquoBiopolymers vol 96 no 3 pp 328ndash339 2011

[46] R D Bremel and E J Homan ldquoAn integrated approach to epit-ope analysis II a system for proteomic-scale prediction ofimmunological characteristicsrdquo Immunome Research vol 6 no1 article 8 2010

[47] C M Diez-Rivero B Chenlo P Zuluaga and P A RecheldquoQuantitative modeling of peptide binding to TAP using sup-port vector machinerdquo Proteins vol 78 no 1 pp 63ndash72 2010

[48] E Martınez-Naves E M Lafuente and P A Reche ldquoRecogni-tion of the ligand-type specificity of classical and non-classicalMHC I proteinsrdquo FEBS Letters vol 585 no 21 pp 3478ndash34842011

[49] M Bhasin E L Reinherz and P A Reche ldquoRecognition andclassification of histones using support vectormachinerdquo Journalof Computational Biology vol 13 no 1 pp 102ndash112 2006

[50] P A Reche and E L Reinherz ldquoPEPVAC a web server formulti-epitope vaccine development based on the prediction ofsupertypic MHC ligandsrdquo Nucleic Acids Research vol 33 no 2pp W138ndashW142 2005

[51] E M Lafuente and P A Reche ldquoPrediction of MHC-peptidebinding a systematic and comprehensive overviewrdquo CurrentPharmaceutical Design vol 15 no 28 pp 3209ndash3220 2009

[52] A Gaulton L J Bellis A P Bento et al et al ldquoA large-scalebioactivity database for drug discoveryrdquoNucleic Acids Researchpp D1100ndashD1107 2012

[53] W H Cropper Great Physicists The Life and Times of LeadingPhysicists from Galileo to Hawking Oxford University PressNew York NY USA 2004

[54] E L Bouzarth A Brooks R Camassa et al ldquoEpicyclic orbits ina viscous fluid about a precessing rod theory and experimentsat the micro-and macro-scalesrdquo Physical Review E vol 76 no1 part 2 Article ID 016313 2007

[55] M Laberge ldquoIntrinsic protein electric fields basic non-covalentinteractions and relationship to protein-induced Stark effectsrdquoBiochimica et BiophysicaActa vol 1386 no 2 pp 305ndash330 1998

[56] FM Fernandez and J AMorales ldquoPerturbation theorywithoutwave functions for the Zeeman effect in hydrogenrdquo PhysicalReview A vol 46 no 1 pp 318ndash326 1992

[57] B D Marshall and W G Chapman ldquoThermodynamic pertur-bation theory for associating fluids with small bond angleseffects of steric hindrance ring formation and double bondingrdquoPhysical Reiew E vol 87 no 5 Article ID 052307 12 pages 2013

[58] H Gonzalez-Diaz S Arrasate A Gomez-San Juan et al ldquoNewtheory for multiple input-output perturbations in complexmolecular systems 1 Linear QSPR electronegativity models inphysical organic and medicinal chemistryrdquo Current Topics inMedicinal Chemistry 2013

[59] H Gonzalez-Diaz S Arrasate N Sotomayor et al ldquoMIANNmodels in medicinal physical and organic chemistryrdquo CurrentTopics in Medicinal Chemistry vol 13 no 5 pp 619ndash641 2013

[60] G E P Box and GM Jenkins Time Series Analysis Forecastingand Control Holden-Day San Francisco Calif USA 1970

[61] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoUnified multi-target approach for the rational insilico design of anti-bladder cancer agentsrdquo Anti-Cancer Agentsin Medicial Chemistry vol 13 no 5 pp 791ndash800 2013

[62] A Speck-Planche V V Kleandrova and M N Cordeiro ldquoNewinsights toward the discovery of antibacterial agents multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugsrdquo TheEuropean Journal of Pharmaceutical Sciences vol 48 no 4-5pp 812ndash818 2013

[63] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoMulti-target inhibitors for proteins associated withAlzheimer in silico discovery using fragment-based descrip-torsrdquo Current Alzheimer Research vol 10 no 2 pp 117ndash1242013

[64] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoIn silico discovery and virtual screening of multi-target inhibitors for proteins in Mycobacterium tuberculosisrdquoCombinatorial Chemistry amp High Throughput Screening vol 15no 8 pp 666ndash673 2012

[65] A Speck-Planche V V Kleandrova F Luan and M N DS Cordeiro ldquoChemoinformatics in anti-cancer chemotherapymulti-target QSAR model for the in silico discovery of anti-breast cancer agentsrdquo The European Journal of PharmaceuticalSciences vol 47 no 1 pp 273ndash279 2012

Submit your manuscripts athttpwwwhindawicom

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Behavioural Neurology

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Disease Markers

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OncologyJournal of

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Oxidative Medicine and Cellular Longevity

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PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of

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Computational and Mathematical Methods in Medicine

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Research and TreatmentAIDS

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Gastroenterology Research and Practice

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Page 2: Research Article Model for Vaccine Design by …downloads.hindawi.com/journals/jir/2014/768515.pdfResearch Article Model for Vaccine Design by Prediction of B-Epitopes of IEDB Given

2 Journal of Immunology Research

used to performmultivariate statistics andor machine learn-ing (ML) analysis in order to preprocess data and later fitthe final QSARQSPR model using techniques like princi-pal component analysis (PCA) linear discriminant analysis(LDA) support vector machine (SVM) or artificial neuralnetworks (ANN) [28]

QSARQSPRmodels are also important in immunoinfor-matics to predict the propensity of different molecular struc-tures to play different roles in immunological processesTheyinclude skin vaccine adjuvants and sensitizers [30ndash38] drugsand their activitytoxicity protein targets in the immunesystem [39] and epitopes [40ndash49] Moreover Reche andReinherz [50] implemented PEPVAC (promiscuous epitope-based vaccine) a web server for the formulation of multi-epitope vaccines that predict peptides binding to five distinctHLA class I supertypes (A2 A3 B7 A24 and B15) PEPVACcan also identify conserved MHC ligands as well as thosewith a C-terminus resulting from proteasomal cleavage TheDana-Farber Cancer Institute hosted the PEPVAC server atthe site httpimmunaxdfciharvardeduPEPVAC To closewith a last example Lafuente and Reche [51] reviewed theavailable methods for predicting MHC-peptide binding anddiscussed their most relevant advantages and drawbacks

In many complex QSPR-like problems in immunoinfor-matics like in other areas we know the exact experimentalresult (known solution) of the problem but we are interestedin the possible result obtained after a change (perturbation)on one or multiple values of the initial conditions of theexperiment (new solution) For instance we often know forlarge collections of 119894th molecules (119898

119894) organic compounds

drugs xenobiotics andor peptide sequences the efficiencyof the compound 120576(119888

119894119895) as adjuvant action as epitope immun-

otoxicity andor the interaction (affinity inhibition etc) withimmunological targets In addition we often known for eachmolecule the exact conditions (119888

119894119895) of assay for the initial

experiment including structure of the molecule 119898119894(drug

adjuvant and sequence of the peptide) source organism (so)host organism (ho) immunological process (ip) experimen-tal technique (tq) concentration temperature time solventsand coadjuvants This is the case of big data retrieved fromvery large databases like IEDB [1ndash4] and CHEMBL [52]However we do not know the possible result of the experi-ment if we change at least one of these conditions (perturba-tion) We refer to small changes or perturbations in bothstructure and condition for input or output variables Itmeans that we include changes in ho so ip and tq changes ofthe compound by one analogue compound with similarstructure changes in the sequence of the epitope (artificial byorganic synthesis or natural mutations) and polarity of thesolvent or coadjuvants In these cases we could use a per-turbation theory model to solve the QSARQSPR problemPerturbation theory includes methods that add ldquosmallrdquo termsto a known solution of a problem in order to approach asolution to a related problemwithout known solution Pertur-bation models have been widely used in all branches ofscience from QM to astronomy and life sciences includingchaos or ldquobutterfly effectrdquo Bohrrsquos atomic theory Heisenbergrsquosmechanics Zeemanrsquos and Starkrsquos effects and other models

with applications in like protein spectroscopy and others [53ndash57] In a very recent work Gonzalez-Diaz et al [58] formu-lated a general-purpose perturbation theory or model formultiple-boundaryQSPRQSARproblems However there isnot report in the immunoinformatics literature of a generalQSPR perturbation model for IEDB B-epitopes Here wereport the first example of QSPR-perturbation model for B-epitopes reported in IEDB able to predict the probability ofoccurrence of an epitope after a perturbation in the sequencethe experimental technique the exposition process andorthe source or host organisms

2 Materials and Methods

21 Molecular Descriptors for Peptides We calculated themolecular descriptors of the structure of peptides using thesoftwareMARCH-INSIDE (MI) based on the algorithmwiththe same name [59] The MI approach uses a Markov Chainmethod to calculate the 119896th mean values of different physico-chemical molecular properties 120582(119898

119894) for 119894th molecules (119898)

These 120582(119898119894) values are calculated as an average of 119896120582(119898119894)

values for all atoms placed at topological distance 119889 le 119896which are in turn the means of atomic properties (120582

119895) for all

atoms in the molecule and its neighbors placed at 119889 = 119896For instance it is possible to derive average estimationsof molecular refractivities 119896MR(119898

119894) partition coefficients

119896119875(119898119894) and hardness 119896120578(119898

119894) for atoms placed at different

topological distances 119889 le 119896 In this first work we calculatedonly one type of 120582(119898

119894) values We calculated for all peptides

the average value 120594(119898119894) of all the atomic electronegativities 120594

119894

for all 120575119894atoms connected to the 119894th atom (119894 rarr 119895) and their

neighbors placed at a distance 119889 le 5 [59]

120594 (119898119894) =

1

6

5

sum

119896=0

119896120594119895=

1

6

5

sum

119896=0

120575119894

sum

119894rarr 119895

119901119896(120594119895) sdot 120594119895 (1)

We calculate the probabilities 119896119901(120582119895) for any atomic prop-

erty including 119896119901(120594119895) using a Markov Chain model for the

gradual effects of the neighboring atoms at different distancesin the molecular backbone This method has been explainedin detail in many previous works so we omit the details here[59]

22 Electronegativity Perturbation Model for Prediction of B-Epitopes Very recently Gonzalez-Diaz et al [58] formulateda general-purpose perturbation theory ormodel formultiple-boundary QSPRQSAR problems We adapted here this newtheory or modeling method to approach to the peptide pre-diction problem from the point of view of perturbation the-ory Let be a set of 119894th peptide molecules denoted as119898

119894with a

value of efficiency 120576119894119895as epitopes experimentally determined

under a set of boundary conditions 119888119895equiv (1198880 1198881 1198882 1198883 119888

119899)

We put the main emphasis here on peptides reported in thedatabase IEDB In this sense the boundary conditions 119888

119895used

here are the same reported in this database 1198880= is the specific

Journal of Immunology Research 3

peptide 1198881= so119895 1198882= ho119895 1198883= ip119895 and 119888

4= tq119895 In general

so is the organism that expresses the peptide (but it caninclude also artificial peptides cellular lines etc) ho is thehost organism exposed to the peptide by means of the bpdetected with tq As our analysis based on the data reportedby IEDB we are unable to work with continuous values ofepitope activity 120576

119894119895 Consequently we have to predict the

discrete function of B-epitope efficiency 120582(120576119894119895) = 1 for epit-

opes reported in the conditions 119888119895and 120582(120576

119894119895) = 0 otherwise

Ourmain aim is to predict the shift or change in a function ofthe output efficiency Δ120582(120576

119894119895) = 120582(120576

119894119895)ref minus 120582(120576119894119895)new that takes

place after a change variation or perturbation (Δ119881) in thestructure andor boundary conditions of a peptide of refer-ence But we know the efficiency of the process of reference120582(120576119894119895)ref in addition to the molecular structure and the set of

conditions 119888119895for initial (reference) and final processes (new)

Consequently to predict Δ120582(120576119894119895) we have to predict only

120582(120576119894119895)new the efficiency function of the new state obtained by

a change in the structure of the peptide andor the boundaryconditions Let Δ119881 be a perturbation in a function 120582 we candefine 119881

119894119895as the state information function for the reference

and new states According to our recent model [58] we canwrite 119881

119894119895as a function of the conditions and structure of the

peptide 119898119894as follows In fact the variational state functions

119881119894119895have to be written in pairs in order to describe the initial

(reference) and final (new) states of a perturbation as follow

119881119894119895= 120582(120576

119894119895)new

minus

4

sum

119895=1

(120582 (119898119894) minus 120582(119888

119894119895)avg)

119881119902119903= 120582(120576

119902119903)refminus

4

sum

119903=1

(120582 (119898119902) minus 120582(119888

119902119903)avg)

(2)

The state function 119899119881119894119895is for the 119894th peptide measured

under a set of 119888119894119895boundary conditions in output final or new

stateThe conjugated state function 119903119881119902119903is for the 119902th peptide

measured under a set of 119888119902119903boundary conditions for the input

initial or reference state The difference Δ119881 between the new(output) state and the reference (input) state is the additiveperturbation [58] Consider

Δ119881 = 119881119894119895minus 119881119902119903= [

[

120582(120576119894119895)new

minus

4

sum

119895=1

(120582 (119898119894) minus 120582(119888

119894119895)avg)]

]

minus [120582(120576119902119903)refminus

4

sum

119903=1

(120582 (119898119902) minus 120582(119888

119902119903)avg)]

(3)

Equation (3) described before opens the door to testdifferent hypothesis A simple hypotheses is H

0 existence of

one small and constant value of the perturbation functionΔ119881 = 119890

0for all the pairs of peptides and a linear relationship

Table 1 Results of QSPR-perturbation model for IEDB B-Epitopes

Data Stat Pred Predicted epitope perturbationssubset param 120582(120576

119894119895) = 1 120582(120576

119894119895) = 0

120582(120576119894119895) = 1 Sp 970 84607 2660

120582(120576119894119895) = 0 Sn 936 4354 63548

Total train Ac 955120582(120576119894119895) = 1 Sp 971 28060 840

120582(120576119894119895) = 0 Sn 933 1485 20641

Total cv Ac 954Bold font is used to highlight the number of cases correctly classified by themodel

between perturbations of inputoutput boundary conditionswith coefficients 119886

119894119895 119887119894119895 119888119902119903 and 119889

119894119895 Consider

1198900= Δ119881

= [

[

119886119894119895sdot 120582(120576119894119895)new

minus

4

sum

119895=1

119887119894119895sdot (120582 (119898

119894) minus 120582(119888

119895)avg)]

]

minus [119888119902119903sdot 120582(120576119902119903)refminus

4

sum

119903=1

119889119902119903sdot (120582 (119898

119902) minus 120582(119888

119903)avg)]

(4)

We can use elemental algebraic operations to obtain fromthese equations an expression for efficiency as epitope of thepeptide 120582(120576

119894119895)new In this case considering 119887

119894119895asymp 119889119902119903 we can

obtain the different expressions the last may be very usefulto solve the QSRR problem for the large datasets formed byIEDB B-epitopes Consider

120582(120576119894119895)new

= (

119888119902119903

119886119894119895

) sdot 120582(120576119902119903)ref

+ [

[

4

sum

119895=1

(

119887119902119903

119886119894119895

) sdot (120582 (119898119894) minus 120582(119888

119895)avg)

new]

]

minus [

4

sum

119903=1

(

119889119902119903

119886119894119895

) sdot (120582 (119898119902) minus 120582(119888

119903)avg)ref

]

+ (

1198900

119886119894119895

)

120582(120576119894119895)new

=10158401198880sdot 120582(120576119902119903)ref

+

4

sum

119895=1

1015840119889119894119895sdot Δ (120582 (119898

119894) minus 120582(119888

119895)avg) +10158401198900

120582(120576119894119895)new

=10158401198880sdot 120582(120576119902119903)ref+

4

sum

119895=1

1015840119889119894119895sdot ΔΔ120582

119894119895119902119903+10158401198900

120582(120576119894119895)new

=10158401198880sdot 120582(120576119902119903)ref+

4

sum

119895=1

1015840119889119894119895sdot ΔΔ120594119894119895119902119903+10158401198900

(5)

4 Journal of Immunology Research

Table 2 Average values and count of input-output cases for differentorganisms process and techniques

Source organism (so) 119873in 119873outlowast120594

Homo sapiens 38920 39274 2685Plasmodium falciparum 10669 9446 2704Hepatitis C virus 9935 10239 2683Bos taurus 5671 5780 2690Canine parvovirus 5655 5637 2693Foot-mouth disease virus 4062 4176 2676Triticum aestivum 3769 3887 2703Bacillus anthracis 3602 3600 2699Human papillomavirus 3316 3414 2693Human herpesvirus 3026 3132 2684Gallus gallus 2850 2829 2689Arachis hypogaea 2648 2670 2687Mycobacterium tuberculosis 2637 2593 2688Clostridium botulinum 2588 2722 2685SARS coronavirus 2550 2704 2686Mus musculus 2334 2287 2682Hepatitis B virus 2007 2066 2680Helicobacter pylori 1958 1796 2695Hevea brasiliensis 1938 1958 2697Hepatitis E virus 1928 1941 2685Shigella flexneri 1878 1701 2699Dengue virus 2 1767 1828 2679Staphylococcus aureus 1757 1661 2694Treponema pallidum 1739 1755 2691Escherichia coli 1721 1678 2689Murine hepatitis virus 1575 1603 2692Haemophilus influenzae 1545 1587 2695Streptococcus mutans 1523 1537 2697Puumala virus (strain) 1505 1574 2689Chlamydia trachomatis 1402 1546 2704Human respiratory virus 1347 1398 2682Borrelia burgdorferi 1228 1237 2698Hepatitis delta virus 1182 1199 2690Streptococcus pyogenes 1181 1251 2697Porphyromonas gingivalis 1143 1085 2688Human enterovirus 1106 1132 2689Influenza A virus 1085 1086 2687Mycoplasma hyopneumoniae 1044 1024 2695Rattus norvegicus 1025 1039 2689Bordetella pertussis 1011 960 2685Human T-lymphotropic virus 996 1031 2680Anaplasma marginale 977 857 2707Measles virus strain 804 810 2688Fasciola hepatica 803 857 2685Neisseria meningitidis 789 853 2696Human poliovirus 766 780 2690Tityus serrulatus 764 775 2680Torpedo californica 752 788 2687

Table 2 Continued

Source organism (so) 119873in 119873outlowast120594

Cryptomeria japonica 719 794 2680Mycobacterium bovis 717 733 2688Trypanosoma cruzi 691 777 2704Andes virus CHI-7913 679 687 2690Bovine papillomavirus 672 665 2692Human hepatitis 670 696 2688Leishmania infantum 659 735 2688Human parvovirus 649 691 2683Poa pratensis 648 664 2692Aspergillus fumigatus 642 709 2677Duck hepatitis 587 603 2688Olea europaea 571 577 2692Porcine reproductive 515 514 2681Fagopyrum esculentum 509 497 2685Juniperus ashei 505 568 2672Mycobacterium leprae 489 542 2690Glycine max 477 509 2685D pteronyssinus 455 464 2680Plasmodium vivax 453 446 2690Chlamydophila pneumoniae 446 462 2690Pseudomonas aeruginosa 443 454 2691Vibrio cholera 427 426 2694Streptococcus sp 426 425 2691Mycobacterium avium 425 415 2689Dermatophagoides farinae 410 390 2693Human coxsackievirus 406 392 2694Equine infectious virus 404 419 2688Babesia equi 383 371 2696Prunus dulcis 383 379 2708Human adenovirus 375 405 2686Theileria parva 366 371 2713Candida albicans 365 370 2690Porcine endogenous 355 351 2692Ovis aries 352 350 2683Chironomus thummi 347 338 2691Sus scrofa 343 362 2686Bovine leukemia virus 333 329 2676Ricinus communis 329 314 2692Androctonus australis 322 357 2685Renibacterium salmoninarum 319 350 2690Orientia tsutsugamushi 309 372 2705Anacardium occidentale 293 306 2693Conus geographus 289 295 2660Host organism (ho) 119873in 119873out

lowast120594

Homo sapiens 257293 91093 26856Mus musculus 107867 51466 26873Oryctolagus cuniculus 65053 31433 26900Bos taurus 15333 2072 26909Rattus norvegicus 9450 3562 26876Aotus sp 9044 3933 26879Sus scrofa 7725 3464 26873Gallus gallus 7507 997 26790

Journal of Immunology Research 5

Table 2 Continued

Source organism (so) 119873in 119873outlowast120594

Canis lupus 6604 3334 26906Macaca mulatta 5261 2569 26993Ovis aries 3953 1653 26836Equus caballus 3943 2099 26842Cavia porcellus 3458 1688 26833Capra hircus 2182 1127 26830Aotus nancymaae 1659 852 26837Pan troglodytes 1614 732 26757Marmota monax 1100 509 27011Felis catus 901 279 26838Myodes glareolus 814 388 26863Anas platyrhynchos 688 342 26880Homo sapiens (human) 508 270 26851Trichosurus vulpecula 456 126 26921Mesocricetus auratus 438 104 26909Macaca cyclopis 382 193 26871O tshawytscha 333 159 26929Macaca fuscata 188 100 26667Cricetulus migratorius 171 142 27008Camelus dromedarius 171 89 26886Dicentrarchus labrax 121 55 26759Macaca fascicularis 96 52 26793Saimiri sciureus 92 44 26900Canis familiaris 77 42 26850Rattus rattus 72 31 26760Callithrix pygmaea 67 30 26920Chinchilla lanigera 41 24 26729Aotus lemurinus 30 19 26860Papio cynocephalus 27 13 27267Aotus griseimembra 26 12 27000Mustela vison 18 10 27000Chlorocebus aethiops 15 10 26875Bos indicus 13 4 26925Oncorhynchus mykiss 10 4 26700M macquariensis 9 6 26600Cricetulus griseus 8 4 26900Aotus trivirgatus 7 4 27000Process type (pt) 119873in 119873out

lowast120594

AID 111197 108536 26876OOID 32419 32617 26868OAID 19210 18954 26801OOA 15863 16303 26902NI 13430 15206 26845EWEIR 4818 4864 26843EEE 3113 3546 26906OOD 2806 2799 26887AICD 1077 1095 26812EWED 696 686 26879DEWED 280 337 26804TT 260 215 26806OOC 153 137 26800

Table 2 Continued

Source organism (so) 119873in 119873outlowast120594

Technique (tq) 119873in 119873outlowast120594

ELISA 133458 135109 26871WI 33627 33292 26887ACAbB 7780 9068 26862PhDIP 7450 4496 26771RIA 5241 5218 26858IFAIH 4454 4581 26879NIAA 4222 4316 26892FIA 2255 2276 26897PAC 1312 1219 26837IP 1127 1089 26886SPR 758 639 26860FACS 608 647 26907Other 502 495 26813SAC 484 393 26878ELISPOT 396 412 26979RDAT 366 323 26859EDAT 284 330 26800XRC 231 227 26880MS 209 179 26849PFF 171 153 26820AbDPO 162 295 26968CdC 146 205 26895IAbBA 144 183 26940IOT 124 106 26835HAGGI 115 122 26834IgMHR 89 90 26929EAAA 84 139 26922HS 82 67 26791AbdCC 73 118 26897AGG 50 60 26980CM 50 57 26863The lowastindicates that quantities like lowast120594 is the average value of the meanelectronegativity (119898119894) for all the peptides in IEDB that are epitopes for thesame boundary condition

3 Results and Discussion

We propose herein for the first time a QSRR-perturbationmodel able to predict variations in the propensity of a peptideto act as B-epitope taking into consideration the propensityof a peptide of reference and the changes in peptide sequenceimmunological process host organism source organismsand the experimental technique used The best QSPR-perturbation model found here with LDA was

120582(120576119894119895)new

= 4979 sdot 120582(120576119894119895)refminus 221642 sdot Δ120594seq

+ 8770 sdot ΔΔ120594ho + 63572 sdot ΔΔ120594so

minus 55387 sdot ΔΔ120594ip + 201919 sdot ΔΔ120594tq minus 2149

119873 = 155169 119877119888 = 092

119880 = 015 119901 lt 001

(6)

6 Journal of Immunology Research

Table3To

p100

values

ofp1

forp

ositive

perturbatio

nsin

training

serie

sNew

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

115153

MKG

VVC

TRIYEK

VHom

osapiens

Hom

osapiens

OAID

ELISA

115155

NNQRK

KAKN

TPFN

MLK

RERN

Mus

musculus

Denguev

irus2

AID

WI

001

0012

000

40017

0012

52124

QQQPP

Hom

osapiens

Triticum

aestivum

OOA

ELISA

52128

QQQQGG

SQSQ

KGKQ

QHom

osapiens

Glycinem

axOOA

WI

00minus0018

00002

3639

APL

GVT

Hom

osapiens

HepatitisE

virus

EWEIR

ELISA

3652

APL

TRG

SCRK

RNRS

PER

Hom

osapiens

Hum

anherpesvirus

OOID

ELISA

004

004

0039

0043

004

135959

LTRA

YAKD

VKF

GHom

osapiens

Hom

osapiens

OAID

ELISA

135963

NGQEE

KAG

VVS

TGLIGGG

Mus

musculus

MD

AID

ELISAminus004minus0038minus0047minus0033minus004

108075

PREP

QVY

Hom

osapiens

Hom

osapiens

OAID

ELISA

108078

PTSP

SGV

EEWIV

TQVVPG

VAOryctolagus

cuniculus

Hom

osapiens

AID

ACAb

Bminus001minus000

6minus001minus0003minus0011

25113

HVVDLP

Hom

osapiens

HepatitisE

virus

EWEIR

ELISA

25126

HWGNH

SKSH

PQR

Mus

musculus

MD

AID

ELISA

002

0022

0013

0023

002

48780

PPFSPQ

Hom

osapiens

Hepatitisd

elta

virus

OOID

ELISA

48782

PPFT

SAV

GGVDHRS

Mus

musculus

MD

AID

SAC

002

0022

0008

0021

0021

40988

LYVVA

YQA

Mus

musculus

Viscum

album

AID

ELISA

4100

4MAARL

CCQLD

PARD

VHom

osapiens

HepatitisB

virus

OOID

ELISA

002

0018

000

60019

002

50439

QDAY

NAAG

Mus

musculus

Mycobacteriu

mscrofulaceum

AID

ELISA

5044

5QDCN

CSI

YPGHAS

GHRM

AWD

Hom

osapiens

HepatitisC

virus

OOID

ELISA

004

0038

0028

0039

004

98849

KIPA

VFK

IDA

Hom

osapiens

Bostaurus

DEW

EDWI

98850

KKGSE

EEGDITNPIN

Hom

osapiens

Arachish

ypogaea

OOA

IFAIHminus005minus005minus0053minus004minus0051

116171

TQDQDP

BBHFF

KNIV

TPR

Hom

osapiens

Hom

osapiens

OAID

ACAb

B116

286

CGKG

LSAT

VTG

GQKG

RGSR

Oryctolagus

cuniculus

Mus

musculus

AID

MS

001

0014

0008

0017

000

9

123442

LLKD

LRKN

Hom

osapiens

Bornadiseasev

irus

EWEIR

WI

123443

LLTE

HR

MTW

DPA

QPP

RDLT

EHom

osapiens

Hom

osapiens

OOID

ELISAminus002minus002minus0025minus0017minus0022

47858

PHVVDL

Hom

osapiens

HepatitisE

virus

EWEIR

ELISA

47860

PHWIK

KPNRQ

GLG

YYS

Caprahircus

Hum

anT-lymphotropic

virus

AID

ACAb

B001

0007

0005

0013

000

9

61783

STNKA

VVS

LSBo

staurus

Bovine

respira

tory

AID

ELISA

61792

STNPK

PQRK

TKRN

TNRR

PQD

Hom

osapiens

HepatitisC

virus

OOID

ELISA

003

0025

0019

0029

003

118210

VMLY

QISEE

Hom

osapiens

Hom

osapiens

OAID

WI

118217

VTK

YITK

GWKE

VH

Oryctolagus

cuniculus

Hom

osapiens

AID

ELISA

005

0054

005

0057

004

8

130944

LFKH

SOryctolagus

cuniculus

Rattu

snorvegicus

AID

ELISA

130956

LPPR

VTP

KWSLDA

WST

WR

Hom

osapiens

MD

OOD

WI

001

000

6minus0002

0011

0012

Journal of Immunology Research 7Ta

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

23028

GVKY

AHom

osapiens

MD

OAID

WI

23032

GVLA

KDVRF

SQV

Hom

osapiens

MD

OOID

ELISA

00

00007minus0002

51199

QKK

AIE

Oryctolagus

cuniculus

Vibriocholerae

AID

ELISA

51204

QKK

NK

RNTN

RRPQ

DV

Hom

osapiens

HepatitisC

virus

OOID

ELISA

003

0026

0018

0029

003

144783

SHVVT

MLD

NF

Hom

osapiens

Hom

osapiens

NI

ELISA

144786

SMNRG

RGTH

PSLIWM

Mus

musculus

MD

AID

ACAb

B003

0032

0023

0033

0029

134343

DLY

IKMus

musculus

Hum

anpapillomaviru

sAID

NIAA

134344

DMAQ

VTV

GPG

LLGVS

TLMus

musculus

Hom

osapiens

AID

WI

00minus000

90

0

38321

LNQLA

GRM

Anas

platyrhynchos

Duckhepatitis

AID

ELISA

38323

LNQTA

RAFP

DCA

ICW

EPSP

POryctolagus

cuniculus

Bovine

leukemia

virus

AID

ACAb

Bminus001minus0008minus0022minus001minus0011

144657

GQITVD

MMYG

Hom

osapiens

Hom

osapiens

OAID

ELISA

1446

61GRE

GYP

ADGGCA

WPA

CYC

Oryctolagus

cuniculus

MD

AID

WI

002

0024

0013

0027

0022

21084

GLQ

NMus

musculus

Chlamydia

trachom

atis

AID

ELISA

21093

GLR

AQD

DFS

GWDI

NTP

AFE

WMus

musculus

Mycobacteriu

mtuberculosis

AID

WI

003

003

0013

003

0032

98453

SGFS

GSV

QFV

Oryctolagus

cuniculus

Neisseria

meningitid

isAID

ELISA

98456

SICS

NN

PTCW

AIC

KRIPNKK

Mus

musculus

Hum

anrespira

tory

virus

AID

IFAIH

004

0037

0027

004

004

1

98453

SGFS

GSV

QFV

Mus

musculus

Neisseria

meningitid

isAID

ELISA

98456

SICS

NN

PTCW

AIC

KRIPNKK

Mus

musculus

Hum

anrespira

tory

virus

AID

IFAIH

004

004

0027

004

004

1

107107

EAIQ

PRa

ttus

norvegicu

sHom

osapiens

AID

ELISA

107110

EKER

RPSP

IGTA

TLL

Hom

osapiens

MD

OOA

ELISA

005

004

80043

0053

005

110857

FTGEA

YSY

WSA

KHom

osapiens

Mycoplasm

apenetra

nsEW

EIR

ELISA

110859

GEE

SRIS

LPLP

NF

SSLN

LRE

Mus

musculus

Hom

osapiens

AID

FIA

00002minus0017

0003

0003

36315

LGSA

YPMus

musculus

Mycobacteriu

mlep

rae

MD

ELISA

36317

LGSG

AFG

TIYK

GMus

musculus

Avian

erythroblasto

sisvirus

AID

ACAb

B001

001

00011

000

9

122034

WNPA

DRa

ttus

norvegicu

sTorpedocalifornica

AID

ELISA

122035

WNPA

DYG

GIK

WN

PADYG

GIK

Rattu

snorvegicu

sMD

AID

RIA

001

001

0001

001

000

9

25013

HVA

DID

KLID

Mus

musculus

Puum

alavirus

Kazan

AID

ELISA

25021

HVA

PTH

YVTE

SDA

SQRV

TQL

Hom

osapiens

HepatitisC

virus

OOID

ELISA

0minus0002minus0014minus0001

0

36162

LGIH

EOryctolagus

cuniculus

Cand

idaalbicans

AID

ELISA

36166

LGIM

GE

YRGTP

RNQDLY

DAA

Mus

musculus

Hum

anrespira

tory

virus

AID

RIA

0minus0003minus0007

0minus0001

67253

TWEV

LHMus

musculus

Plasmodium

vivax

AID

ELISA

67257

TWGEN

ETDVLL

LNNTR

PPQ

Hom

osapiens

HepatitisC

virus

OOID

ACAb

Bminus002minus0022minus0027minus0021minus0021

8 Journal of Immunology Research

Table3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

50990

QGYR

VSSY

LPHom

osapiens

Hevea

brasiliensis

OOA

WI

50998

QHEQ

DR

PTPS

PAP

SRPF

SVL

Hom

osapiens

HepatitisE

virus

OOID

ELISA

001

001minus0002

0007

0008

100458

RDVLQ

LYAPE

Mus

musculus

Bacillusa

nthracis

AID

ELISA

1004

62RF

STRY

GNQNGRI

RVLQ

RFD

Hom

osapiens

Arachish

ypogaea

EWED

ELISA

003

0028

0018

003

003

111036

TEST

FTGEA

YSV

Hom

osapiens

Mycoplasm

apenetra

nsEW

EIR

ELISA

111039

TGVPID

PAVPD

SSIV

PLLE

SBo

staurus

Bovine

papillomaviru

sAID

ELISA

003

0035

002

0033

003

117919

IFIEME

Hom

osapiens

Hom

osapiens

OAID

WI

117921

IGIIDLIE

KRKF

NQ

Mus

musculus

Hom

osapiens

AID

WI

003

0032

003

0037

003

7127

CTDTD

KLF

Oryctolagus

cuniculus

Shigellaflexneri

AID

ELISA

7128

CTDVS

TAIH

ADQL

TPAW

Hom

osapiens

SARS

coronavirus

OOID

ELISA

001

000

6minus0003

000

9001

112253

PGQSP

KLHom

osapiens

Hom

osapiens

OAID

ELISA

112255

PIRA

LVGDEV

ELP

CRISPG

KMus

musculus

Hom

osapiens

AID

ELISA

001

0012

001

0017

001

122034

WNPA

DRa

ttus

norvegicu

sTorpedocalifornica

AID

ELISA

122038

WNPD

DY

GGVKW

NP

DDYG

GVK

Rattu

snorvegicu

sMD

AID

RIA

00minus000

90minus0001

131878

FLMLV

GGST

LHom

osapiens

Hom

osapiens

OAID

ACAb

B131879

FLVA

HT

RARA

PSA

GER

ARR

SMus

musculus

Mus

musculus

AID

NIAA

003

0032

0028

0037

0033

7066

4VQ

VVYD

YQHom

osapiens

Treponem

apallidu

mOOID

ELISA

70667

VQWMNR

LIAFA

FAG

NHVS

PHom

osapiens

HepatitisC

virus

OOID

ELISA

005

005

004

1005

005

71545

VTV

Hom

osapiens

Helicobacterpylori

OOID

ELISA

71559

VTV

RGGL

RILSPD

RKHom

osapiens

Arachish

ypogaea

OOA

WI

004

004

0032

0043

004

2

127856

TDVRY

KD

Mus

musculus

Mus

musculus

AID

ACAb

B127857

TDVRY

KDDMYH

FFCP

AIQ

AQMus

musculus

Mus

musculus

AID

PFF

001

001

001

001

000

6

112149

GVG

WIRQ

Hom

osapiens

Hom

osapiens

OAID

ELISA

112152

HHPA

RTAHYG

SLP

QKS

HGRT

Hom

osapiens

Hom

osapiens

AID

ELISA

00

00007

0

119581

FSCS

VMHE

Hom

osapiens

Hom

osapiens

OAID

ELISA

119582

GLQ

LIQL

INVDEV

NQI

Mus

musculus

Hom

osapiens

AID

RIAminus001minus0008minus001minus0003minus0011

144657

GQITVD

MMYG

Hom

osapiens

Hom

osapiens

OAID

ELISA

144659

GRE

GYP

ADGGAA

GYC

NTE

Oryctolagus

cuniculus

MD

AID

WIminus001minus000

6minus0017minus0003minus0008

25013

HVA

DI

DKL

IDMus

musculus

Puum

alavirus

Kazan

AID

ELISA

25022

HVA

PTH

YVVES

DA

SQRV

TQV

Hom

osapiens

HepatitisC

virus

OOID

ELISA

0minus0002minus0014minus0001

0

Journal of Immunology Research 9Ta

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

144652

GMRG

MKG

LVY

Hom

osapiens

Hom

osapiens

OAID

ELISA

144654

GPH

PTLE

VVPM

GRG

SMus

musculus

MD

AID

ELISAminus002minus0018minus0027minus0013minus002

104515

HDCR

PKKI

Mus

musculus

LaCrossevirus

AID

IFAIH

104521

IGTL

KKIL

DET

VKD

KIAKE

QRa

ttus

norvegicu

sStreptococcus

pyogenes

AID

ELISAminus004minus004minus0053minus004minus004

1

7367

CYGDWA

Hom

osapiens

Triticum

aestivum

OOA

ELISA

7374

CYGLP

DS

EPTK

TNGK

Mus

musculus

Tityus

serrulatus

AID

WIminus002minus0018minus0043minus0023minus0018

7367

CYGDWA

Hom

osapiens

Triticum

aestivum

OOA

ELISA

7374

CYGLP

DS

EPTK

TNGK

Mus

musculus

Tityus

serrulatus

AID

WIminus002minus0018minus0043minus0023minus0018

144610

DFF

TYK

Mus

musculus

Porcine

transmissible

AID

WI

144611

DFN

GSF

DMNGTITA

Oryctolagus

cuniculus

Escherich

iacoli

AID

ELISAminus001minus0007minus0018minus001minus0012

112047

AST

RESG

Hom

osapiens

Hom

osapiens

OAID

ELISA

112048

ATAST

MDHARH

GF

LPRH

RDT

Hom

osapiens

Hom

osapiens

AID

ELISA

005

005

005

0057

005

36136

LGGVFT

Hom

osapiens

Denguev

irus2

OOID

ELISA

36137

LGGWKL

QSD

PRAY

AL

Hom

osapiens

Ambrosia

artemisiifolia

OOA

RIA

001

001

0007

0013

000

9

115256

FREL

KDLK

GY

Hom

osapiens

Bostaurus

DEW

EDWI

115261

GDLE

ILL

QKW

ENG

ECAQ

KKI

Hom

osapiens

Bostaurus

OOA

FIAminus001minus001minus001

0minus000

9

129024

KADQLY

KHom

osapiens

Hom

osapiens

OAID

ELISA

129026

KAKK

PAAAAG

AKK

AKS

Oryctolagus

cuniculus

Hom

osapiens

AID

ELISA

003

0034

003

0037

003

148481

YTRD

LVYK

Rattu

snorvegicu

sHom

osapiens

AID

WI

148483

YVPIVT

FYSE

ISM

HSSRA

IPOryctolagus

cuniculus

MD

AID

ELISA

00002minus0007

0minus0002

150850

GY

Mus

musculus

Hum

anpapillomaviru

sAID

ELISA

150853

HIG

GLSI

LDPIFG

VL

Hom

osapiens

Dermatophagoides

farin

aeOOA

ACAb

B004

0038

004

0043

0039

107366

FPPK

PKD

Hom

osapiens

Hom

osapiens

OAID

ELISA

107376

GDRS

GYS

SPGSP

GMus

musculus

Hom

osapiens

AID

ACAb

Bminus003minus0028minus003minus0023minus0031

114859

ICGTD

GVTY

THom

osapiens

Gallusgallus

OOA

WI

114865

IVER

ETR

GQSE

NPL

WHALR

RRa

ttus

norvegicu

sHum

anherpesvirus

AID

ELISA

004

004

20035

0037

0038

62149

SVHLF

Hom

osapiens

MD

OAID

WI

62150

SVIALG

SQEG

ALH

QALA

GAI

Equu

scaballus

WestN

ilevirus

AID

IFAIHminus002minus0021minus0012minus0013minus0021

98455

SGSV

QFV

PIQ

Mus

musculus

Neisseria

meningitid

isAID

ELISA

98456

SICS

NNP

TCWAICK

RIPN

KKMus

musculus

Hum

anrespira

tory

virus

AID

IFAIH

004

004

0027

004

004

1

61783

STNKA

VVS

LSBo

staurus

Bovine

respira

tory

AID

ELISA

61791

STNPK

PQRK

TKRN

TNRR

PQHom

osapiens

HepatitisC

virus

EWEIR

ACAb

B004

0035

0029

0037

0039

100318

NAPK

TFQ

FIN

Mus

musculus

Bacillusa

nthracis

AID

ELISA

100319

NASSEL

HLL

GFG

INAEN

NHR

Hom

osapiens

Arachish

ypogaea

EWED

ELISA

0minus0002minus0012

00

10 Journal of Immunology ResearchTa

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

118947

PFSA

PPPA

Hom

osapiens

Hom

osapiens

OAID

ELISA

118948

PGAIEQG

PADDPG

EGPS

TGP

Hom

osapiens

Hum

anherpesvirus

NI

ACAb

Bminus004minus004minus004

1minus0036minus004

1

78323

YSFR

DMus

musculus

Bluetongue

virus1

AID

ELISA

78341

AALT

AEN

TAIK

KRN

ADAKA

Hom

osapiens

Streptococcus

mutan

sEE

EEL

ISA

001

0008

0007

0013

001

145831

IPLG

TRP

Mus

musculus

Hum

anpapillomaviru

sAID

ELISA

145841

KEDFR

YAISST

NEI

GLL

GA

Susscrofa

Classicalsw

ine

AID

PACminus004minus004minus0045minus004minus0043

119592

HTF

PAVLQ

Hom

osapiens

Hom

osapiens

OAID

ELISA

119596

IHIPSE

KIWRP

DLV

LYMus

musculus

Hom

osapiens

AID

RIA

001

0012

001

0017

000

9

58780

SKAANLSIIK

MD

Beetnecrotic

MD

WI

58783

SKAFSN

CYPY

DVP

DYA

SLOryctolagus

cuniculus

Influ

enza

Avirus

AID

RIAminus001minus000

4minus0014minus000

9minus0013

115153

MKG

VVC

TRIYEK

VHom

osapiens

Hom

osapiens

NI

ELISA

115155

NNQRK

KAKN

TPFN

MLK

RERN

Mus

musculus

Denguev

irus2

AID

ELISA

001

0012

000

40013

001

133629

LPLR

FOryctolagus

cuniculus

Gallusgallus

AID

ACAb

B133630

LPPG

LHV

FPLA

SNRS

Mus

musculus

MD

AID

SPRminus001minus0013minus0021minus001minus001

96215

EEEE

AE

DKE

DHom

osapiens

Hom

osapiens

OAID

ELISA

96216

EEEG

LLK

KSADTL

WNMQK

Mus

musculus

Mus

musculus

AID

ELISA

008

0082

0078

0087

008

39782

LTAASV

Hom

osapiens

Triticum

aestivum

OOA

ELISA

39788

LTAEL

KIYS

VIQ

AEI

NKH

LOryctolagus

cuniculus

Yersiniapestis

AID

ACAb

B001

0014minus0001

0007

000

9

107479

KFNWYV

DHom

osapiens

Hom

osapiens

OAID

ELISA

107482

KGEP

GL

PGRG

FPGFP

Mus

musculus

Hom

osapiens

AID

ACAb

B0

0002

00007minus0001

63569

TETV

NSD

IMacaca

mulatta

Shigellaflexneri

AID

ELISA

63573

TEVEL

KER

KHRIED

AVRN

AK

Hom

osapiens

Mycobacteriu

mlep

rae

OOID

ELISA

005

0036

004

10049

005

134028

DDTIS

Mus

musculus

Hom

osapiens

AID

NIAA

134029

DED

ENQS

PRSFQKK

TROryctolagus

cuniculus

Hom

osapiens

AID

ELISA

005

0053

005

005

004

8

23028

GVKY

AHom

osapiens

MD

OAID

WI

23032

GVLA

KDV

RFSQ

VHom

osapiens

MD

EWEIR

ACAb

B0

00

000

4minus0003

115293

IMCV

KKILDK

Hom

osapiens

Bostaurus

DEW

EDWI

115295

INPS

KEN

LCST

FCK

EVVRN

AHom

osapiens

Bostaurus

OOA

FIAminus003minus003minus003minus002minus0029

65105

TLTP

ENTL

Mus

musculus

Shigellaflexneri

AID

ELISA

65110

TLTS

GSD

LDRC

TTFD

DV

Oryctolagus

cuniculus

SARS

coronavirus

AID

ELISA

001

0013minus0003

001

001

1344

71PK

PEQ

Mus

musculus

Streptococcus

pneumoniae

AID

FACS

1344

72PL

LPGT

STTS

TGPC

KTHom

osapiens

HepatitisB

virus

AID

ELISA

002

0018

0019

002

0016

Journal of Immunology Research 11Ta

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

142228

LYCY

EQLN

DSSE

Hom

osapiens

Hum

anpapillomaviru

sNI

ELISA

142250

NWGDEP

SKRR

DRS

NSR

GRK

NFelis

catus

Felin

einfectio

usAID

ELISA

007

006

80071

0073

007

21549

GNYD

FWYQ

SHom

osapiens

Staphylococcus

aureus

OOID

ELISA

21553

GNYN

YKY

RYLR

HGK

LRPF

ERMus

musculus

SARS

coronavirus

AID

ELISA

003

0032

0021

0031

003

21549

GNYD

FWYQ

SHom

osapiens

Staphylococcus

aureus

OOID

ELISA

21553

GNYN

YKY

RYLR

HGK

LRPF

EROryctolagus

cuniculus

SARS

coronavirus

AID

ELISA

003

0034

0021

0031

003

34908

LAPL

GE

Hom

osapiens

HepatitisE

virus

EWEIR

ELISA

34914

LAPS

TLRS

LRKR

RLSSP

Hom

osapiens

Hum

anherpesvirus

OOID

ELISA

006

006

0059

0063

006

130454

CLFP

NNSY

CMus

musculus

MD

AID

NIAA

130456

CRPQ

VN

NPK

EWS

CAAC

Hom

osapiens

MD

OOD

ACAb

B001

0008

001

0011

0007

1964

4GFV

PSM

Hom

osapiens

Hepatitisd

elta

virus

OOID

ELISA

1964

7GFV

SASI

FGFQ

AEV

GPN

NTR

Oryctolagus

cuniculus

VacciniavirusW

RAID

ELISAminus001minus000

6minus0013minus000

9minus001

20678

GKRP

EMus

musculus

Streptococcus

pyogenes

AID

WI

20680

GKS

KRD

AKN

NAAK

LAVDKL

LMus

musculus

VacciniavirusW

RAID

WI

001

001

0001

001

001

123278

GYL

KDLP

TTOvisa

ries

Fasciolahepatica

AID

ELISA

123282

HAC

QKK

LLKF

EAL

QQEE

GEE

Rattu

snorvegicu

sGallusgallus

AID

PFFminus002minus0016minus0016minus002minus0025

141067

PLSLEP

DP

Mus

musculus

Hom

osapiens

AID

FACS

141073

REGVRW

RVMAIQ

Mus

musculus

Hom

osapiens

AID

ELISA

006

006

006

006

0056

139248

SFAG

TVIE

Mus

musculus

Classicalsw

ine

AID

WI

139305

TAAQ

ITQ

RKWEA

ARE

AEQ

RROryctolagus

cuniculus

Hom

osapiens

AID

IP003

0033

0026

003

003

104515

HDCR

PKKI

Mus

musculus

LaCrossevirus

AID

IFAIH

104520

IAKE

QE

NKE

TIGT

LKKI

LDE

Rattu

snorvegicu

sStreptococcus

pyogenes

AID

ELISAminus006minus006minus0073minus006minus0061

113517

HLY

ADGLT

DMus

musculus

Hum

anpapillomaviru

sAID

IFAIH

113518

HNKI

QA

IELE

DLL

RYS

KLYR

Mus

musculus

Hom

osapiens

AID

ELISA

002

002

0011

002

0019

156970

VER

HQ

Hom

osapiens

Hom

osapiens

OAID

ELISA

156975

WSSTV

LRVS

PTRT

VP

Mus

musculus

MD

AID

ELISA

001

0012

0003

0017

001

78252

PVQNLT

Mus

musculus

Porphyromonas

gingivalis

AID

WI

78253

QGGCG

RGWAFS

ATG

AIEA

Mus

musculus

Glycinem

axAID

ELISA

002

002

0017

002

0018

6068

CCPD

KNKS

Mus

musculus

Hum

anherpesvirus

AID

WI

6074

CCRH

KQKD

VGDVK

QTL

PPS

Ovisa

ries

MD

AID

ELISA

001

000

6000

4001

0008

12 Journal of Immunology Research

Table3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

53109

RAGVC

YHom

osapiens

Triticum

aestivum

OOA

ELISA

53116

RAILTA

FSPA

QDI

WGTS

Oryctolagus

cuniculus

SARS

coronavirus

AID

ELISAminus002minus0016minus0037minus0023minus002

147041

IPEQ

Hom

osapiens

Triticum

aestivum

OOA

WI

147064

KHQGA

QYV

WN

RTA

Hom

osapiens

Bostaurus

OOID

WI

006

006

0047

0057

006

7066

4VQ

VVYD

YQOryctolagus

cuniculus

Treponem

apallidu

mAID

ELISA

70667

VQWMN

RLIAFA

FAG

NHVS

PHom

osapiens

HepatitisC

virus

OOID

ELISA

005

004

6004

10049

005

134343

DLY

IKMus

musculus

Hum

anpapillomaviru

sAID

PhDIP

134344

DMAQ

VTV

GPG

LLGVS

TLMus

musculus

Hom

osapiens

AID

PhDIP

00minus000

90

0

Journal of Immunology Research 13

The first input term is the value 120582(120576119894119895)ref is the scoring

function 120582 of the efficiency of the initial process 120576119894119895(known

solution) The function 120582(120576119894119895)ref = 1 if the 119894th peptide could

experimentally be demonstrated to be a B-epitope in theassay of reference (reference) carried out in the conditions119888119895 120582(120576119894119895)ref = 0 otherwise The variational-perturbation

terms ΔΔ120594119888119895are at the same time terms typical of pertur-

bation theory and moving average (MA) functions used inBox-Jenkin models in time series [60] These new types ofterms account both for the deviation of the electronegativityof all amino acids in the sequence of the new peptidewith respect to the peptide of reference and with respectto all boundary conditions In Table 1 we give the overallclassification results obtainedwith thismodel Speck-Plancheet al [61ndash63] introduced different multitargetmultiplexingQSARmodels that incorporate this type of information basedon MAs The results obtained with the present model areexcellent comparedwith other similarmodels in the literatureuseful for other problems including moving average models[64 65] or perturbation models [58] Notably this is also thefirst model combining both perturbation theory and MAs ina QSPR context

The other input terms are the following The first Δ120594seq =120594(119898119902)ref minus120594(119898119894)new is the perturbation term for the variation

or in themean value of electronegativity for all amino acids inthe sequence of the peptide of reference The remnant inputvariables of the model ΔΔ120594

119888119895= Δ120594

119888119895-ref minus Δ120594119888119895-new =

[120594(119898119902)ref minus

lowast120594 (119888119902119903)ref] minus [120594(119898119894)new minus

lowast120594 (119888119894119895)new] quantify

values of the conditions of the new assay 119888119895-new that representperturbations with respect to the initial conditions 119888

119894119895-ref of

the assay of reference The quantities lowast120594(119888119894119895) and lowast120594(119888

119902119903) are

the average values of the mean electronegativity values 120594(119898119894)

and 120594(119898119902) for all new and reference peptides in IEDB that

are epitopes under the 119895th or 119903th boundary condition Thevalues of these terms have been tabulated for gt500 sourceorganisms gt50 host organisms gt10 biological process andgt30 experimental techniques We must substitute the valuesof 120594(119898

119894) and 120594(119898

119902) of the new and reference peptides and the

tabulated values of lowast120594(119888119894119895) and lowast120594(119888

119902119903) for all combinations

of boundary conditions to predict the perturbations of theaction as epitope of peptides In doing so we can found theoptimal sequence and boundary conditions towards the useof the peptide in the development of a vaccine In Table 2 wegive some of these values of lowast120594(119888

119894119895) and lowast120594(119888

119902119903)

In Table 3 we depict the sequences and input-outputboundary conditions for top perturbations present in IEDBAll these perturbations have observed value of 120582(120576

119894119895)new = 1

and predicted value also equal to 1 with a high probabilitySee Supplementary Material available online at httpdxdoiorg1011552014768515 file contains a full list of gt200000cases of perturbations

4 Conclusions

It is possible to develop generalmodels for vaccine design ableto predict the results of multiple input-output perturbations

in peptide sequence and experimental assay boundary condi-tions using ideas of QSPR analysis perturbation theory andBox and Jenkins MA operators The electronegativity valuescalculated withMARCH-INSIDE seem to be goodmoleculardescriptors for this type of QSPR-perturbation models

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

The present study was partially supported by GrantsAGL2010-22290-C02 and AGL2011-30563-C03 from Minis-terio de Ciencia e Innovacion Spain and Grant CN 2012155from Xunta de Galicia Spain

References

[1] B Peters J Sidney P Bourne et al ldquoThe design and implemen-tation of the immune epitope database and analysis resourcerdquoImmunogenetics vol 57 no 5 pp 326ndash336 2005

[2] B Peters J Sidney P Bourne et al ldquoThe immune epitopedatabase and analysis resource from vision to blueprintrdquo PLoSBiology vol 3 no 3 p e91 2005

[3] PWang A AMorgan Q Zhang A Sette and B Peters ldquoAuto-mating document classification for the Immune Epitope Data-baserdquo BMC Bioinformatics vol 8 article 269 2007

[4] A Sette ldquoThe immune epitope database and analysis resourcefrom vision to blueprintrdquo Genome Informatics vol 15 no 2 p299 2004

[5] N Salimi W Fleri B Peters and A Sette ldquoThe immune epi-tope database a historical retrospective of the first decaderdquoImmunology vol 137 no 2 pp 117ndash123 2012

[6] Y Kim A Sette and B Peters ldquoApplications for T-cell epitopequeries and tools in the Immune EpitopeDatabase andAnalysisResourcerdquo Journal of Immunological Methods vol 374 no 1-2pp 62ndash69 2011

[7] Y Kim J Ponomarenko Z Zhu D Tamang et al ldquoImmuneepitope database analysis resourcerdquo Nucleic Acids Research ppW525ndashW530 2012

[8] A M Helguera R D Combes M P Gonzalez and M N DS Cordeiro ldquoApplications of 2D descriptors in drug design aDRAGON talerdquo Current Topics in Medicinal Chemistry vol 8no 18 pp 1628ndash1655 2008

[9] GM Casanola-Martın Y Marrero-Ponce M T H Khan et alldquoDragon method for finding novel tyrosinase inhibitors Biosil-ico identification and experimental in vitro assaysrdquo The Euro-pean Journal of Medicinal Chemistry vol 42 no 11-12 pp 1370ndash1381 2007

[10] I V Tetko J Gasteiger R Todeschini et al ldquoVirtual computa-tional chemistry laboratorymdashdesign and descriptionrdquo Journalof Computer-AidedMolecular Design vol 19 no 6 pp 453ndash4632005

[11] A R Katritzky A Oliferenko A Lomaka and M KarelsonldquoSix-membered cyclic ureas as HIV-1 protease inhibitors aQSAR study based on CODESSA PRO approachrdquo Bioorganicand Medicinal Chemistry Letters vol 12 no 23 pp 3453ndash34572002

14 Journal of Immunology Research

[12] A R Katritzky S Perumal R Petrukhin and E Klein-peter ldquoCODESSA-based theoretical QSPRmodel for hydantoinHPLC-RT lipophilicitiesrdquo Journal of Chemical Information andComputer Sciences vol 41 no 3 pp 569ndash574 2001

[13] S Vilar G Cozza and S Moro ldquoMedicinal chemistry and themolecular operating environment (MOE) application of QSARand molecular docking to drug discoveryrdquo Current Topics inMedicinal Chemistry vol 8 no 18 pp 1555ndash1572 2008

[14] G Marzaro A Chilin A Guiotto et al ldquoUsing the TOPS-MODE approach to fit multi-target QSAR models for tyrosinekinases inhibitorsrdquo The European Journal of Medicinal Chem-istry vol 46 no 6 pp 2185ndash2192 2011

[15] S Vilar E Estrada E Uriarte L Santana and Y Gutierrez ldquoInsilico studies toward the discovery of new anti-HIV nucleosidecompounds through the use of tops-mode and 2D3D con-nectivity indices 2 Purine derivativesrdquo Journal of ChemicalInformation and Modeling vol 45 no 2 pp 502ndash514 2005

[16] E Estrada J A Quincoces and G Patlewicz ldquoCreating molec-ular diversity from antioxidants in Brazilian propolis combina-tion of TOPS-MODE QSAR and virtual structure generationrdquoMolecular Diversity vol 8 no 1 pp 21ndash33 2004

[17] E Estrada andHGonzalez ldquoWhat are the limits of applicabilityfor graph theoretic descriptors in QSPRQSAR modelingdipole moments of aromatic compounds with TOPS-MODEdescriptorsrdquo Journal of Chemical Information and ComputerSciences vol 43 no 1 pp 75ndash84 2003

[18] Y Marrero-Ponce J A Castillo-Garit E Olazabal et al ldquoTom-ocomd-Cardd a novel approach for computer-aided ldquorationalrdquodrug design I Theoretical and experimental assessment of apromising method for computational screening and in silicodesign of new anthelmintic compoundsrdquo Journal of Computer-Aided Molecular Design vol 18 no 10 pp 615ndash634 2004

[19] Y Marrero Ponce R Medina Marrero E A Castro et al ldquoPro-tein quadratic indices of the ldquomacromolecular pseudographrsquos 120572-carbon atom adjacency matrixrdquo 1 prediction of arc repressoralanine-mutantrsquos stabilityrdquoMolecules vol 9 no 12 pp 1124ndash11472004

[20] H Gonzalez-Dıaz F Prado-Prado and F M Ubeira ldquoPredict-ing antimicrobial drugs and targets with the MARCH-INSIDEapproachrdquo Current Topics in Medicinal Chemistry vol 8 no 18pp 1676ndash1690 2008

[21] R Garcıa-Domenech J Galvez J V de Julian-Ortiz and LPogliani ldquoSomenew trends in chemical graph theoryrdquoChemicalReviews vol 108 no 3 pp 1127ndash1169 2008

[22] E Estrada and E Uriarte ldquoRecent advances on the role of topo-logical indices in drug discovery researchrdquo Current MedicinalChemistry vol 8 no 13 pp 1573ndash1588 2001

[23] R Todeschini andVConsonniHandbook ofMolecularDescrip-tors Wiley-VCH Weinheim Germany 2008

[24] E Estrada E J Delgado J B Alderete and G A Jana ldquoQuan-tum-connectivity descriptors inmodeling solubility of environ-mentally important organic compoundsrdquo Journal of Computa-tional Chemistry vol 25 no 14 pp 1787ndash1796 2004

[25] E Besalu X Girones L Amat andR Carbo-Dorca ldquoMolecularquantum similarity and the fundamentals of QSARrdquo Accountsof Chemical Research vol 35 no 5 pp 289ndash295 2002

[26] D A Rincon M N D S Cordeiro and R A Mosquera ldquoOnthe electronic structure of cocaine and its metabolitesrdquo Journalof Physical Chemistry A vol 113 no 50 pp 13937ndash13942 2009

[27] M Mandado M J Gonzalez-Moa and R A MosqueraldquoChemical graph theory and n-center electron delocalization

indices a study on polycyclic aromatic hydrocarbonsrdquo Journalof Computational Chemistry vol 28 no 10 pp 1625ndash1633 2007

[28] THill andP Lewicki STATISTICSMethods andApplications AComprehensive Reference for Science Industry and DataMiningStatSoft Tulsa Okla USA 2006

[29] E Frank M Hall L Trigg G Holmes and I H Witten ldquoDatamining in bioinformatics using Wekardquo Bioinformatics vol 20no 15 pp 2479ndash2481 2004

[30] G Patlewicz N Ball E D Booth E Hulzebos E Zvinavasheand C Hennes ldquoUse of category approaches read-across and(Q)SAR general considerationsrdquo Regulatory Toxicology andPharmacology vol 67 no 1 pp 1ndash12 2013

[31] D W Roberts and G Y Patlewicz ldquoUpdating the skin sensiti-zation in vitro data assessment paradigm in 2009mdasha chemistryand QSAR perspectiverdquo Journal of Applied Toxicology vol 30no 3 pp 286ndash288 2010

[32] D W Roberts and G Y Patlewicz ldquoNonanimal alternatives forskin sensitization letter to the editorrdquoToxicological Sciences vol106 no 2 pp 572ndash574 2008

[33] E Estrada G Patlewicz and Y Gutierrez ldquoFrom knowledgegeneration to knowledge archive a general strategy usingTOPS-MODE with DEREK to formulate new alerts for skinsensitizationrdquo Journal of Chemical Information and ComputerSciences vol 44 no 2 pp 688ndash698 2004

[34] G F Gerberick C A Ryan P S Kern et al ldquoA chemical datasetfor evaluation of alternative approaches to skin-sensitizationtestingrdquo Contact Dermatitis vol 50 no 5 pp 274ndash288 2004

[35] G Y Patlewicz D A Basketter C K Smith Pease et al ldquoFurtherevaluation of quantitative structurendashactivity relationship mod-els for the prediction of the skin sensitization potency ofselected fragrance allergensrdquo Contact Dermatitis vol 50 no 2pp 91ndash97 2004

[36] E Estrada G Patlewicz M Chamberlain D Basketter andS Larbey ldquoComputer-aided knowledge generation for under-standing skin sensitization mechanisms the TOPS-MODEapproachrdquo Chemical Research in Toxicology vol 16 no 10 pp1226ndash1235 2003

[37] G Patlewicz R Rodford and J D Walker ldquoQuantitative struc-ture-activity relationships for predicting skin and eye irritationrdquoEnvironmental Toxicology and Chemistry vol 22 no 8 pp1862ndash1869 2003

[38] G Y Patlewicz R A Rodford G Ellis and M D Barratt ldquoAQSAR model for the eye irritation of cationic surfactantsrdquo Tox-icology In Vitro vol 14 no 1 pp 79ndash84 2000

[39] E Tenorio-Borroto C G Penuelas Rivas J C VasquezChagoyan N Castanedo F J Prado-Prado and X Garcia-Mera ldquoANN multiplexing model of drugs effect on macro-phages theoretical and flow cytometry study on the cyto-toxicity of the anti-microbial drug G1 in spleenrdquo Bioorganic ampMedicinal Chemistry vol 20 no 20 pp 6181ndash6194 2012

[40] D R Flower H McSparron M J Blythe et al ldquoComputationalvaccinology quantitative approachesrdquo Novartis FoundationSymposium vol 254 pp 102ndash120 2003

[41] I A Doytchinova P Guan and D R Flower ldquoQuantitativestructure-activity relationships and the prediction of MHCsupermotifsrdquoMethods vol 34 no 4 pp 444ndash453 2004

[42] Y Xiao and M R Segal ldquoPrediction of genomewide conservedepitope profiles of HIV-1 classifier choice and peptide rep-resentationrdquo Statistical Applications in Genetics and MolecularBiology vol 4 no 1 article 25 2005

Journal of Immunology Research 15

[43] T Fagerberg V Zoete S Viatte P Baumgaertner P M Alves PRomero et al ldquoPrediction of cross-recognition of peptide-HLAA2 by melan-a-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationshipsrdquo PLoSONE vol 8 no 7 Article ID e65590 2013

[44] D Barh A N Misra A Kumar and A Vasco ldquoA novel strategyof epitope design inNeisseria gonorrhoeaerdquo Bioinformation vol5 no 2 pp 77ndash82 2010

[45] J Bi R Song H Yang et al ldquoStepwise identification of HLA-Alowast0201-restricted CD8+ T-cell epitope peptides from herpessimplex virus type 1 genome boosted by a steprank schemerdquoBiopolymers vol 96 no 3 pp 328ndash339 2011

[46] R D Bremel and E J Homan ldquoAn integrated approach to epit-ope analysis II a system for proteomic-scale prediction ofimmunological characteristicsrdquo Immunome Research vol 6 no1 article 8 2010

[47] C M Diez-Rivero B Chenlo P Zuluaga and P A RecheldquoQuantitative modeling of peptide binding to TAP using sup-port vector machinerdquo Proteins vol 78 no 1 pp 63ndash72 2010

[48] E Martınez-Naves E M Lafuente and P A Reche ldquoRecogni-tion of the ligand-type specificity of classical and non-classicalMHC I proteinsrdquo FEBS Letters vol 585 no 21 pp 3478ndash34842011

[49] M Bhasin E L Reinherz and P A Reche ldquoRecognition andclassification of histones using support vectormachinerdquo Journalof Computational Biology vol 13 no 1 pp 102ndash112 2006

[50] P A Reche and E L Reinherz ldquoPEPVAC a web server formulti-epitope vaccine development based on the prediction ofsupertypic MHC ligandsrdquo Nucleic Acids Research vol 33 no 2pp W138ndashW142 2005

[51] E M Lafuente and P A Reche ldquoPrediction of MHC-peptidebinding a systematic and comprehensive overviewrdquo CurrentPharmaceutical Design vol 15 no 28 pp 3209ndash3220 2009

[52] A Gaulton L J Bellis A P Bento et al et al ldquoA large-scalebioactivity database for drug discoveryrdquoNucleic Acids Researchpp D1100ndashD1107 2012

[53] W H Cropper Great Physicists The Life and Times of LeadingPhysicists from Galileo to Hawking Oxford University PressNew York NY USA 2004

[54] E L Bouzarth A Brooks R Camassa et al ldquoEpicyclic orbits ina viscous fluid about a precessing rod theory and experimentsat the micro-and macro-scalesrdquo Physical Review E vol 76 no1 part 2 Article ID 016313 2007

[55] M Laberge ldquoIntrinsic protein electric fields basic non-covalentinteractions and relationship to protein-induced Stark effectsrdquoBiochimica et BiophysicaActa vol 1386 no 2 pp 305ndash330 1998

[56] FM Fernandez and J AMorales ldquoPerturbation theorywithoutwave functions for the Zeeman effect in hydrogenrdquo PhysicalReview A vol 46 no 1 pp 318ndash326 1992

[57] B D Marshall and W G Chapman ldquoThermodynamic pertur-bation theory for associating fluids with small bond angleseffects of steric hindrance ring formation and double bondingrdquoPhysical Reiew E vol 87 no 5 Article ID 052307 12 pages 2013

[58] H Gonzalez-Diaz S Arrasate A Gomez-San Juan et al ldquoNewtheory for multiple input-output perturbations in complexmolecular systems 1 Linear QSPR electronegativity models inphysical organic and medicinal chemistryrdquo Current Topics inMedicinal Chemistry 2013

[59] H Gonzalez-Diaz S Arrasate N Sotomayor et al ldquoMIANNmodels in medicinal physical and organic chemistryrdquo CurrentTopics in Medicinal Chemistry vol 13 no 5 pp 619ndash641 2013

[60] G E P Box and GM Jenkins Time Series Analysis Forecastingand Control Holden-Day San Francisco Calif USA 1970

[61] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoUnified multi-target approach for the rational insilico design of anti-bladder cancer agentsrdquo Anti-Cancer Agentsin Medicial Chemistry vol 13 no 5 pp 791ndash800 2013

[62] A Speck-Planche V V Kleandrova and M N Cordeiro ldquoNewinsights toward the discovery of antibacterial agents multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugsrdquo TheEuropean Journal of Pharmaceutical Sciences vol 48 no 4-5pp 812ndash818 2013

[63] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoMulti-target inhibitors for proteins associated withAlzheimer in silico discovery using fragment-based descrip-torsrdquo Current Alzheimer Research vol 10 no 2 pp 117ndash1242013

[64] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoIn silico discovery and virtual screening of multi-target inhibitors for proteins in Mycobacterium tuberculosisrdquoCombinatorial Chemistry amp High Throughput Screening vol 15no 8 pp 666ndash673 2012

[65] A Speck-Planche V V Kleandrova F Luan and M N DS Cordeiro ldquoChemoinformatics in anti-cancer chemotherapymulti-target QSAR model for the in silico discovery of anti-breast cancer agentsrdquo The European Journal of PharmaceuticalSciences vol 47 no 1 pp 273ndash279 2012

Submit your manuscripts athttpwwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MEDIATORSINFLAMMATION

of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Behavioural Neurology

EndocrinologyInternational Journal of

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Disease Markers

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BioMed Research International

OncologyJournal of

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Oxidative Medicine and Cellular Longevity

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PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational and Mathematical Methods in Medicine

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Research and TreatmentAIDS

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Gastroenterology Research and Practice

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Page 3: Research Article Model for Vaccine Design by …downloads.hindawi.com/journals/jir/2014/768515.pdfResearch Article Model for Vaccine Design by Prediction of B-Epitopes of IEDB Given

Journal of Immunology Research 3

peptide 1198881= so119895 1198882= ho119895 1198883= ip119895 and 119888

4= tq119895 In general

so is the organism that expresses the peptide (but it caninclude also artificial peptides cellular lines etc) ho is thehost organism exposed to the peptide by means of the bpdetected with tq As our analysis based on the data reportedby IEDB we are unable to work with continuous values ofepitope activity 120576

119894119895 Consequently we have to predict the

discrete function of B-epitope efficiency 120582(120576119894119895) = 1 for epit-

opes reported in the conditions 119888119895and 120582(120576

119894119895) = 0 otherwise

Ourmain aim is to predict the shift or change in a function ofthe output efficiency Δ120582(120576

119894119895) = 120582(120576

119894119895)ref minus 120582(120576119894119895)new that takes

place after a change variation or perturbation (Δ119881) in thestructure andor boundary conditions of a peptide of refer-ence But we know the efficiency of the process of reference120582(120576119894119895)ref in addition to the molecular structure and the set of

conditions 119888119895for initial (reference) and final processes (new)

Consequently to predict Δ120582(120576119894119895) we have to predict only

120582(120576119894119895)new the efficiency function of the new state obtained by

a change in the structure of the peptide andor the boundaryconditions Let Δ119881 be a perturbation in a function 120582 we candefine 119881

119894119895as the state information function for the reference

and new states According to our recent model [58] we canwrite 119881

119894119895as a function of the conditions and structure of the

peptide 119898119894as follows In fact the variational state functions

119881119894119895have to be written in pairs in order to describe the initial

(reference) and final (new) states of a perturbation as follow

119881119894119895= 120582(120576

119894119895)new

minus

4

sum

119895=1

(120582 (119898119894) minus 120582(119888

119894119895)avg)

119881119902119903= 120582(120576

119902119903)refminus

4

sum

119903=1

(120582 (119898119902) minus 120582(119888

119902119903)avg)

(2)

The state function 119899119881119894119895is for the 119894th peptide measured

under a set of 119888119894119895boundary conditions in output final or new

stateThe conjugated state function 119903119881119902119903is for the 119902th peptide

measured under a set of 119888119902119903boundary conditions for the input

initial or reference state The difference Δ119881 between the new(output) state and the reference (input) state is the additiveperturbation [58] Consider

Δ119881 = 119881119894119895minus 119881119902119903= [

[

120582(120576119894119895)new

minus

4

sum

119895=1

(120582 (119898119894) minus 120582(119888

119894119895)avg)]

]

minus [120582(120576119902119903)refminus

4

sum

119903=1

(120582 (119898119902) minus 120582(119888

119902119903)avg)]

(3)

Equation (3) described before opens the door to testdifferent hypothesis A simple hypotheses is H

0 existence of

one small and constant value of the perturbation functionΔ119881 = 119890

0for all the pairs of peptides and a linear relationship

Table 1 Results of QSPR-perturbation model for IEDB B-Epitopes

Data Stat Pred Predicted epitope perturbationssubset param 120582(120576

119894119895) = 1 120582(120576

119894119895) = 0

120582(120576119894119895) = 1 Sp 970 84607 2660

120582(120576119894119895) = 0 Sn 936 4354 63548

Total train Ac 955120582(120576119894119895) = 1 Sp 971 28060 840

120582(120576119894119895) = 0 Sn 933 1485 20641

Total cv Ac 954Bold font is used to highlight the number of cases correctly classified by themodel

between perturbations of inputoutput boundary conditionswith coefficients 119886

119894119895 119887119894119895 119888119902119903 and 119889

119894119895 Consider

1198900= Δ119881

= [

[

119886119894119895sdot 120582(120576119894119895)new

minus

4

sum

119895=1

119887119894119895sdot (120582 (119898

119894) minus 120582(119888

119895)avg)]

]

minus [119888119902119903sdot 120582(120576119902119903)refminus

4

sum

119903=1

119889119902119903sdot (120582 (119898

119902) minus 120582(119888

119903)avg)]

(4)

We can use elemental algebraic operations to obtain fromthese equations an expression for efficiency as epitope of thepeptide 120582(120576

119894119895)new In this case considering 119887

119894119895asymp 119889119902119903 we can

obtain the different expressions the last may be very usefulto solve the QSRR problem for the large datasets formed byIEDB B-epitopes Consider

120582(120576119894119895)new

= (

119888119902119903

119886119894119895

) sdot 120582(120576119902119903)ref

+ [

[

4

sum

119895=1

(

119887119902119903

119886119894119895

) sdot (120582 (119898119894) minus 120582(119888

119895)avg)

new]

]

minus [

4

sum

119903=1

(

119889119902119903

119886119894119895

) sdot (120582 (119898119902) minus 120582(119888

119903)avg)ref

]

+ (

1198900

119886119894119895

)

120582(120576119894119895)new

=10158401198880sdot 120582(120576119902119903)ref

+

4

sum

119895=1

1015840119889119894119895sdot Δ (120582 (119898

119894) minus 120582(119888

119895)avg) +10158401198900

120582(120576119894119895)new

=10158401198880sdot 120582(120576119902119903)ref+

4

sum

119895=1

1015840119889119894119895sdot ΔΔ120582

119894119895119902119903+10158401198900

120582(120576119894119895)new

=10158401198880sdot 120582(120576119902119903)ref+

4

sum

119895=1

1015840119889119894119895sdot ΔΔ120594119894119895119902119903+10158401198900

(5)

4 Journal of Immunology Research

Table 2 Average values and count of input-output cases for differentorganisms process and techniques

Source organism (so) 119873in 119873outlowast120594

Homo sapiens 38920 39274 2685Plasmodium falciparum 10669 9446 2704Hepatitis C virus 9935 10239 2683Bos taurus 5671 5780 2690Canine parvovirus 5655 5637 2693Foot-mouth disease virus 4062 4176 2676Triticum aestivum 3769 3887 2703Bacillus anthracis 3602 3600 2699Human papillomavirus 3316 3414 2693Human herpesvirus 3026 3132 2684Gallus gallus 2850 2829 2689Arachis hypogaea 2648 2670 2687Mycobacterium tuberculosis 2637 2593 2688Clostridium botulinum 2588 2722 2685SARS coronavirus 2550 2704 2686Mus musculus 2334 2287 2682Hepatitis B virus 2007 2066 2680Helicobacter pylori 1958 1796 2695Hevea brasiliensis 1938 1958 2697Hepatitis E virus 1928 1941 2685Shigella flexneri 1878 1701 2699Dengue virus 2 1767 1828 2679Staphylococcus aureus 1757 1661 2694Treponema pallidum 1739 1755 2691Escherichia coli 1721 1678 2689Murine hepatitis virus 1575 1603 2692Haemophilus influenzae 1545 1587 2695Streptococcus mutans 1523 1537 2697Puumala virus (strain) 1505 1574 2689Chlamydia trachomatis 1402 1546 2704Human respiratory virus 1347 1398 2682Borrelia burgdorferi 1228 1237 2698Hepatitis delta virus 1182 1199 2690Streptococcus pyogenes 1181 1251 2697Porphyromonas gingivalis 1143 1085 2688Human enterovirus 1106 1132 2689Influenza A virus 1085 1086 2687Mycoplasma hyopneumoniae 1044 1024 2695Rattus norvegicus 1025 1039 2689Bordetella pertussis 1011 960 2685Human T-lymphotropic virus 996 1031 2680Anaplasma marginale 977 857 2707Measles virus strain 804 810 2688Fasciola hepatica 803 857 2685Neisseria meningitidis 789 853 2696Human poliovirus 766 780 2690Tityus serrulatus 764 775 2680Torpedo californica 752 788 2687

Table 2 Continued

Source organism (so) 119873in 119873outlowast120594

Cryptomeria japonica 719 794 2680Mycobacterium bovis 717 733 2688Trypanosoma cruzi 691 777 2704Andes virus CHI-7913 679 687 2690Bovine papillomavirus 672 665 2692Human hepatitis 670 696 2688Leishmania infantum 659 735 2688Human parvovirus 649 691 2683Poa pratensis 648 664 2692Aspergillus fumigatus 642 709 2677Duck hepatitis 587 603 2688Olea europaea 571 577 2692Porcine reproductive 515 514 2681Fagopyrum esculentum 509 497 2685Juniperus ashei 505 568 2672Mycobacterium leprae 489 542 2690Glycine max 477 509 2685D pteronyssinus 455 464 2680Plasmodium vivax 453 446 2690Chlamydophila pneumoniae 446 462 2690Pseudomonas aeruginosa 443 454 2691Vibrio cholera 427 426 2694Streptococcus sp 426 425 2691Mycobacterium avium 425 415 2689Dermatophagoides farinae 410 390 2693Human coxsackievirus 406 392 2694Equine infectious virus 404 419 2688Babesia equi 383 371 2696Prunus dulcis 383 379 2708Human adenovirus 375 405 2686Theileria parva 366 371 2713Candida albicans 365 370 2690Porcine endogenous 355 351 2692Ovis aries 352 350 2683Chironomus thummi 347 338 2691Sus scrofa 343 362 2686Bovine leukemia virus 333 329 2676Ricinus communis 329 314 2692Androctonus australis 322 357 2685Renibacterium salmoninarum 319 350 2690Orientia tsutsugamushi 309 372 2705Anacardium occidentale 293 306 2693Conus geographus 289 295 2660Host organism (ho) 119873in 119873out

lowast120594

Homo sapiens 257293 91093 26856Mus musculus 107867 51466 26873Oryctolagus cuniculus 65053 31433 26900Bos taurus 15333 2072 26909Rattus norvegicus 9450 3562 26876Aotus sp 9044 3933 26879Sus scrofa 7725 3464 26873Gallus gallus 7507 997 26790

Journal of Immunology Research 5

Table 2 Continued

Source organism (so) 119873in 119873outlowast120594

Canis lupus 6604 3334 26906Macaca mulatta 5261 2569 26993Ovis aries 3953 1653 26836Equus caballus 3943 2099 26842Cavia porcellus 3458 1688 26833Capra hircus 2182 1127 26830Aotus nancymaae 1659 852 26837Pan troglodytes 1614 732 26757Marmota monax 1100 509 27011Felis catus 901 279 26838Myodes glareolus 814 388 26863Anas platyrhynchos 688 342 26880Homo sapiens (human) 508 270 26851Trichosurus vulpecula 456 126 26921Mesocricetus auratus 438 104 26909Macaca cyclopis 382 193 26871O tshawytscha 333 159 26929Macaca fuscata 188 100 26667Cricetulus migratorius 171 142 27008Camelus dromedarius 171 89 26886Dicentrarchus labrax 121 55 26759Macaca fascicularis 96 52 26793Saimiri sciureus 92 44 26900Canis familiaris 77 42 26850Rattus rattus 72 31 26760Callithrix pygmaea 67 30 26920Chinchilla lanigera 41 24 26729Aotus lemurinus 30 19 26860Papio cynocephalus 27 13 27267Aotus griseimembra 26 12 27000Mustela vison 18 10 27000Chlorocebus aethiops 15 10 26875Bos indicus 13 4 26925Oncorhynchus mykiss 10 4 26700M macquariensis 9 6 26600Cricetulus griseus 8 4 26900Aotus trivirgatus 7 4 27000Process type (pt) 119873in 119873out

lowast120594

AID 111197 108536 26876OOID 32419 32617 26868OAID 19210 18954 26801OOA 15863 16303 26902NI 13430 15206 26845EWEIR 4818 4864 26843EEE 3113 3546 26906OOD 2806 2799 26887AICD 1077 1095 26812EWED 696 686 26879DEWED 280 337 26804TT 260 215 26806OOC 153 137 26800

Table 2 Continued

Source organism (so) 119873in 119873outlowast120594

Technique (tq) 119873in 119873outlowast120594

ELISA 133458 135109 26871WI 33627 33292 26887ACAbB 7780 9068 26862PhDIP 7450 4496 26771RIA 5241 5218 26858IFAIH 4454 4581 26879NIAA 4222 4316 26892FIA 2255 2276 26897PAC 1312 1219 26837IP 1127 1089 26886SPR 758 639 26860FACS 608 647 26907Other 502 495 26813SAC 484 393 26878ELISPOT 396 412 26979RDAT 366 323 26859EDAT 284 330 26800XRC 231 227 26880MS 209 179 26849PFF 171 153 26820AbDPO 162 295 26968CdC 146 205 26895IAbBA 144 183 26940IOT 124 106 26835HAGGI 115 122 26834IgMHR 89 90 26929EAAA 84 139 26922HS 82 67 26791AbdCC 73 118 26897AGG 50 60 26980CM 50 57 26863The lowastindicates that quantities like lowast120594 is the average value of the meanelectronegativity (119898119894) for all the peptides in IEDB that are epitopes for thesame boundary condition

3 Results and Discussion

We propose herein for the first time a QSRR-perturbationmodel able to predict variations in the propensity of a peptideto act as B-epitope taking into consideration the propensityof a peptide of reference and the changes in peptide sequenceimmunological process host organism source organismsand the experimental technique used The best QSPR-perturbation model found here with LDA was

120582(120576119894119895)new

= 4979 sdot 120582(120576119894119895)refminus 221642 sdot Δ120594seq

+ 8770 sdot ΔΔ120594ho + 63572 sdot ΔΔ120594so

minus 55387 sdot ΔΔ120594ip + 201919 sdot ΔΔ120594tq minus 2149

119873 = 155169 119877119888 = 092

119880 = 015 119901 lt 001

(6)

6 Journal of Immunology Research

Table3To

p100

values

ofp1

forp

ositive

perturbatio

nsin

training

serie

sNew

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

115153

MKG

VVC

TRIYEK

VHom

osapiens

Hom

osapiens

OAID

ELISA

115155

NNQRK

KAKN

TPFN

MLK

RERN

Mus

musculus

Denguev

irus2

AID

WI

001

0012

000

40017

0012

52124

QQQPP

Hom

osapiens

Triticum

aestivum

OOA

ELISA

52128

QQQQGG

SQSQ

KGKQ

QHom

osapiens

Glycinem

axOOA

WI

00minus0018

00002

3639

APL

GVT

Hom

osapiens

HepatitisE

virus

EWEIR

ELISA

3652

APL

TRG

SCRK

RNRS

PER

Hom

osapiens

Hum

anherpesvirus

OOID

ELISA

004

004

0039

0043

004

135959

LTRA

YAKD

VKF

GHom

osapiens

Hom

osapiens

OAID

ELISA

135963

NGQEE

KAG

VVS

TGLIGGG

Mus

musculus

MD

AID

ELISAminus004minus0038minus0047minus0033minus004

108075

PREP

QVY

Hom

osapiens

Hom

osapiens

OAID

ELISA

108078

PTSP

SGV

EEWIV

TQVVPG

VAOryctolagus

cuniculus

Hom

osapiens

AID

ACAb

Bminus001minus000

6minus001minus0003minus0011

25113

HVVDLP

Hom

osapiens

HepatitisE

virus

EWEIR

ELISA

25126

HWGNH

SKSH

PQR

Mus

musculus

MD

AID

ELISA

002

0022

0013

0023

002

48780

PPFSPQ

Hom

osapiens

Hepatitisd

elta

virus

OOID

ELISA

48782

PPFT

SAV

GGVDHRS

Mus

musculus

MD

AID

SAC

002

0022

0008

0021

0021

40988

LYVVA

YQA

Mus

musculus

Viscum

album

AID

ELISA

4100

4MAARL

CCQLD

PARD

VHom

osapiens

HepatitisB

virus

OOID

ELISA

002

0018

000

60019

002

50439

QDAY

NAAG

Mus

musculus

Mycobacteriu

mscrofulaceum

AID

ELISA

5044

5QDCN

CSI

YPGHAS

GHRM

AWD

Hom

osapiens

HepatitisC

virus

OOID

ELISA

004

0038

0028

0039

004

98849

KIPA

VFK

IDA

Hom

osapiens

Bostaurus

DEW

EDWI

98850

KKGSE

EEGDITNPIN

Hom

osapiens

Arachish

ypogaea

OOA

IFAIHminus005minus005minus0053minus004minus0051

116171

TQDQDP

BBHFF

KNIV

TPR

Hom

osapiens

Hom

osapiens

OAID

ACAb

B116

286

CGKG

LSAT

VTG

GQKG

RGSR

Oryctolagus

cuniculus

Mus

musculus

AID

MS

001

0014

0008

0017

000

9

123442

LLKD

LRKN

Hom

osapiens

Bornadiseasev

irus

EWEIR

WI

123443

LLTE

HR

MTW

DPA

QPP

RDLT

EHom

osapiens

Hom

osapiens

OOID

ELISAminus002minus002minus0025minus0017minus0022

47858

PHVVDL

Hom

osapiens

HepatitisE

virus

EWEIR

ELISA

47860

PHWIK

KPNRQ

GLG

YYS

Caprahircus

Hum

anT-lymphotropic

virus

AID

ACAb

B001

0007

0005

0013

000

9

61783

STNKA

VVS

LSBo

staurus

Bovine

respira

tory

AID

ELISA

61792

STNPK

PQRK

TKRN

TNRR

PQD

Hom

osapiens

HepatitisC

virus

OOID

ELISA

003

0025

0019

0029

003

118210

VMLY

QISEE

Hom

osapiens

Hom

osapiens

OAID

WI

118217

VTK

YITK

GWKE

VH

Oryctolagus

cuniculus

Hom

osapiens

AID

ELISA

005

0054

005

0057

004

8

130944

LFKH

SOryctolagus

cuniculus

Rattu

snorvegicus

AID

ELISA

130956

LPPR

VTP

KWSLDA

WST

WR

Hom

osapiens

MD

OOD

WI

001

000

6minus0002

0011

0012

Journal of Immunology Research 7Ta

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

23028

GVKY

AHom

osapiens

MD

OAID

WI

23032

GVLA

KDVRF

SQV

Hom

osapiens

MD

OOID

ELISA

00

00007minus0002

51199

QKK

AIE

Oryctolagus

cuniculus

Vibriocholerae

AID

ELISA

51204

QKK

NK

RNTN

RRPQ

DV

Hom

osapiens

HepatitisC

virus

OOID

ELISA

003

0026

0018

0029

003

144783

SHVVT

MLD

NF

Hom

osapiens

Hom

osapiens

NI

ELISA

144786

SMNRG

RGTH

PSLIWM

Mus

musculus

MD

AID

ACAb

B003

0032

0023

0033

0029

134343

DLY

IKMus

musculus

Hum

anpapillomaviru

sAID

NIAA

134344

DMAQ

VTV

GPG

LLGVS

TLMus

musculus

Hom

osapiens

AID

WI

00minus000

90

0

38321

LNQLA

GRM

Anas

platyrhynchos

Duckhepatitis

AID

ELISA

38323

LNQTA

RAFP

DCA

ICW

EPSP

POryctolagus

cuniculus

Bovine

leukemia

virus

AID

ACAb

Bminus001minus0008minus0022minus001minus0011

144657

GQITVD

MMYG

Hom

osapiens

Hom

osapiens

OAID

ELISA

1446

61GRE

GYP

ADGGCA

WPA

CYC

Oryctolagus

cuniculus

MD

AID

WI

002

0024

0013

0027

0022

21084

GLQ

NMus

musculus

Chlamydia

trachom

atis

AID

ELISA

21093

GLR

AQD

DFS

GWDI

NTP

AFE

WMus

musculus

Mycobacteriu

mtuberculosis

AID

WI

003

003

0013

003

0032

98453

SGFS

GSV

QFV

Oryctolagus

cuniculus

Neisseria

meningitid

isAID

ELISA

98456

SICS

NN

PTCW

AIC

KRIPNKK

Mus

musculus

Hum

anrespira

tory

virus

AID

IFAIH

004

0037

0027

004

004

1

98453

SGFS

GSV

QFV

Mus

musculus

Neisseria

meningitid

isAID

ELISA

98456

SICS

NN

PTCW

AIC

KRIPNKK

Mus

musculus

Hum

anrespira

tory

virus

AID

IFAIH

004

004

0027

004

004

1

107107

EAIQ

PRa

ttus

norvegicu

sHom

osapiens

AID

ELISA

107110

EKER

RPSP

IGTA

TLL

Hom

osapiens

MD

OOA

ELISA

005

004

80043

0053

005

110857

FTGEA

YSY

WSA

KHom

osapiens

Mycoplasm

apenetra

nsEW

EIR

ELISA

110859

GEE

SRIS

LPLP

NF

SSLN

LRE

Mus

musculus

Hom

osapiens

AID

FIA

00002minus0017

0003

0003

36315

LGSA

YPMus

musculus

Mycobacteriu

mlep

rae

MD

ELISA

36317

LGSG

AFG

TIYK

GMus

musculus

Avian

erythroblasto

sisvirus

AID

ACAb

B001

001

00011

000

9

122034

WNPA

DRa

ttus

norvegicu

sTorpedocalifornica

AID

ELISA

122035

WNPA

DYG

GIK

WN

PADYG

GIK

Rattu

snorvegicu

sMD

AID

RIA

001

001

0001

001

000

9

25013

HVA

DID

KLID

Mus

musculus

Puum

alavirus

Kazan

AID

ELISA

25021

HVA

PTH

YVTE

SDA

SQRV

TQL

Hom

osapiens

HepatitisC

virus

OOID

ELISA

0minus0002minus0014minus0001

0

36162

LGIH

EOryctolagus

cuniculus

Cand

idaalbicans

AID

ELISA

36166

LGIM

GE

YRGTP

RNQDLY

DAA

Mus

musculus

Hum

anrespira

tory

virus

AID

RIA

0minus0003minus0007

0minus0001

67253

TWEV

LHMus

musculus

Plasmodium

vivax

AID

ELISA

67257

TWGEN

ETDVLL

LNNTR

PPQ

Hom

osapiens

HepatitisC

virus

OOID

ACAb

Bminus002minus0022minus0027minus0021minus0021

8 Journal of Immunology Research

Table3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

50990

QGYR

VSSY

LPHom

osapiens

Hevea

brasiliensis

OOA

WI

50998

QHEQ

DR

PTPS

PAP

SRPF

SVL

Hom

osapiens

HepatitisE

virus

OOID

ELISA

001

001minus0002

0007

0008

100458

RDVLQ

LYAPE

Mus

musculus

Bacillusa

nthracis

AID

ELISA

1004

62RF

STRY

GNQNGRI

RVLQ

RFD

Hom

osapiens

Arachish

ypogaea

EWED

ELISA

003

0028

0018

003

003

111036

TEST

FTGEA

YSV

Hom

osapiens

Mycoplasm

apenetra

nsEW

EIR

ELISA

111039

TGVPID

PAVPD

SSIV

PLLE

SBo

staurus

Bovine

papillomaviru

sAID

ELISA

003

0035

002

0033

003

117919

IFIEME

Hom

osapiens

Hom

osapiens

OAID

WI

117921

IGIIDLIE

KRKF

NQ

Mus

musculus

Hom

osapiens

AID

WI

003

0032

003

0037

003

7127

CTDTD

KLF

Oryctolagus

cuniculus

Shigellaflexneri

AID

ELISA

7128

CTDVS

TAIH

ADQL

TPAW

Hom

osapiens

SARS

coronavirus

OOID

ELISA

001

000

6minus0003

000

9001

112253

PGQSP

KLHom

osapiens

Hom

osapiens

OAID

ELISA

112255

PIRA

LVGDEV

ELP

CRISPG

KMus

musculus

Hom

osapiens

AID

ELISA

001

0012

001

0017

001

122034

WNPA

DRa

ttus

norvegicu

sTorpedocalifornica

AID

ELISA

122038

WNPD

DY

GGVKW

NP

DDYG

GVK

Rattu

snorvegicu

sMD

AID

RIA

00minus000

90minus0001

131878

FLMLV

GGST

LHom

osapiens

Hom

osapiens

OAID

ACAb

B131879

FLVA

HT

RARA

PSA

GER

ARR

SMus

musculus

Mus

musculus

AID

NIAA

003

0032

0028

0037

0033

7066

4VQ

VVYD

YQHom

osapiens

Treponem

apallidu

mOOID

ELISA

70667

VQWMNR

LIAFA

FAG

NHVS

PHom

osapiens

HepatitisC

virus

OOID

ELISA

005

005

004

1005

005

71545

VTV

Hom

osapiens

Helicobacterpylori

OOID

ELISA

71559

VTV

RGGL

RILSPD

RKHom

osapiens

Arachish

ypogaea

OOA

WI

004

004

0032

0043

004

2

127856

TDVRY

KD

Mus

musculus

Mus

musculus

AID

ACAb

B127857

TDVRY

KDDMYH

FFCP

AIQ

AQMus

musculus

Mus

musculus

AID

PFF

001

001

001

001

000

6

112149

GVG

WIRQ

Hom

osapiens

Hom

osapiens

OAID

ELISA

112152

HHPA

RTAHYG

SLP

QKS

HGRT

Hom

osapiens

Hom

osapiens

AID

ELISA

00

00007

0

119581

FSCS

VMHE

Hom

osapiens

Hom

osapiens

OAID

ELISA

119582

GLQ

LIQL

INVDEV

NQI

Mus

musculus

Hom

osapiens

AID

RIAminus001minus0008minus001minus0003minus0011

144657

GQITVD

MMYG

Hom

osapiens

Hom

osapiens

OAID

ELISA

144659

GRE

GYP

ADGGAA

GYC

NTE

Oryctolagus

cuniculus

MD

AID

WIminus001minus000

6minus0017minus0003minus0008

25013

HVA

DI

DKL

IDMus

musculus

Puum

alavirus

Kazan

AID

ELISA

25022

HVA

PTH

YVVES

DA

SQRV

TQV

Hom

osapiens

HepatitisC

virus

OOID

ELISA

0minus0002minus0014minus0001

0

Journal of Immunology Research 9Ta

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

144652

GMRG

MKG

LVY

Hom

osapiens

Hom

osapiens

OAID

ELISA

144654

GPH

PTLE

VVPM

GRG

SMus

musculus

MD

AID

ELISAminus002minus0018minus0027minus0013minus002

104515

HDCR

PKKI

Mus

musculus

LaCrossevirus

AID

IFAIH

104521

IGTL

KKIL

DET

VKD

KIAKE

QRa

ttus

norvegicu

sStreptococcus

pyogenes

AID

ELISAminus004minus004minus0053minus004minus004

1

7367

CYGDWA

Hom

osapiens

Triticum

aestivum

OOA

ELISA

7374

CYGLP

DS

EPTK

TNGK

Mus

musculus

Tityus

serrulatus

AID

WIminus002minus0018minus0043minus0023minus0018

7367

CYGDWA

Hom

osapiens

Triticum

aestivum

OOA

ELISA

7374

CYGLP

DS

EPTK

TNGK

Mus

musculus

Tityus

serrulatus

AID

WIminus002minus0018minus0043minus0023minus0018

144610

DFF

TYK

Mus

musculus

Porcine

transmissible

AID

WI

144611

DFN

GSF

DMNGTITA

Oryctolagus

cuniculus

Escherich

iacoli

AID

ELISAminus001minus0007minus0018minus001minus0012

112047

AST

RESG

Hom

osapiens

Hom

osapiens

OAID

ELISA

112048

ATAST

MDHARH

GF

LPRH

RDT

Hom

osapiens

Hom

osapiens

AID

ELISA

005

005

005

0057

005

36136

LGGVFT

Hom

osapiens

Denguev

irus2

OOID

ELISA

36137

LGGWKL

QSD

PRAY

AL

Hom

osapiens

Ambrosia

artemisiifolia

OOA

RIA

001

001

0007

0013

000

9

115256

FREL

KDLK

GY

Hom

osapiens

Bostaurus

DEW

EDWI

115261

GDLE

ILL

QKW

ENG

ECAQ

KKI

Hom

osapiens

Bostaurus

OOA

FIAminus001minus001minus001

0minus000

9

129024

KADQLY

KHom

osapiens

Hom

osapiens

OAID

ELISA

129026

KAKK

PAAAAG

AKK

AKS

Oryctolagus

cuniculus

Hom

osapiens

AID

ELISA

003

0034

003

0037

003

148481

YTRD

LVYK

Rattu

snorvegicu

sHom

osapiens

AID

WI

148483

YVPIVT

FYSE

ISM

HSSRA

IPOryctolagus

cuniculus

MD

AID

ELISA

00002minus0007

0minus0002

150850

GY

Mus

musculus

Hum

anpapillomaviru

sAID

ELISA

150853

HIG

GLSI

LDPIFG

VL

Hom

osapiens

Dermatophagoides

farin

aeOOA

ACAb

B004

0038

004

0043

0039

107366

FPPK

PKD

Hom

osapiens

Hom

osapiens

OAID

ELISA

107376

GDRS

GYS

SPGSP

GMus

musculus

Hom

osapiens

AID

ACAb

Bminus003minus0028minus003minus0023minus0031

114859

ICGTD

GVTY

THom

osapiens

Gallusgallus

OOA

WI

114865

IVER

ETR

GQSE

NPL

WHALR

RRa

ttus

norvegicu

sHum

anherpesvirus

AID

ELISA

004

004

20035

0037

0038

62149

SVHLF

Hom

osapiens

MD

OAID

WI

62150

SVIALG

SQEG

ALH

QALA

GAI

Equu

scaballus

WestN

ilevirus

AID

IFAIHminus002minus0021minus0012minus0013minus0021

98455

SGSV

QFV

PIQ

Mus

musculus

Neisseria

meningitid

isAID

ELISA

98456

SICS

NNP

TCWAICK

RIPN

KKMus

musculus

Hum

anrespira

tory

virus

AID

IFAIH

004

004

0027

004

004

1

61783

STNKA

VVS

LSBo

staurus

Bovine

respira

tory

AID

ELISA

61791

STNPK

PQRK

TKRN

TNRR

PQHom

osapiens

HepatitisC

virus

EWEIR

ACAb

B004

0035

0029

0037

0039

100318

NAPK

TFQ

FIN

Mus

musculus

Bacillusa

nthracis

AID

ELISA

100319

NASSEL

HLL

GFG

INAEN

NHR

Hom

osapiens

Arachish

ypogaea

EWED

ELISA

0minus0002minus0012

00

10 Journal of Immunology ResearchTa

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

118947

PFSA

PPPA

Hom

osapiens

Hom

osapiens

OAID

ELISA

118948

PGAIEQG

PADDPG

EGPS

TGP

Hom

osapiens

Hum

anherpesvirus

NI

ACAb

Bminus004minus004minus004

1minus0036minus004

1

78323

YSFR

DMus

musculus

Bluetongue

virus1

AID

ELISA

78341

AALT

AEN

TAIK

KRN

ADAKA

Hom

osapiens

Streptococcus

mutan

sEE

EEL

ISA

001

0008

0007

0013

001

145831

IPLG

TRP

Mus

musculus

Hum

anpapillomaviru

sAID

ELISA

145841

KEDFR

YAISST

NEI

GLL

GA

Susscrofa

Classicalsw

ine

AID

PACminus004minus004minus0045minus004minus0043

119592

HTF

PAVLQ

Hom

osapiens

Hom

osapiens

OAID

ELISA

119596

IHIPSE

KIWRP

DLV

LYMus

musculus

Hom

osapiens

AID

RIA

001

0012

001

0017

000

9

58780

SKAANLSIIK

MD

Beetnecrotic

MD

WI

58783

SKAFSN

CYPY

DVP

DYA

SLOryctolagus

cuniculus

Influ

enza

Avirus

AID

RIAminus001minus000

4minus0014minus000

9minus0013

115153

MKG

VVC

TRIYEK

VHom

osapiens

Hom

osapiens

NI

ELISA

115155

NNQRK

KAKN

TPFN

MLK

RERN

Mus

musculus

Denguev

irus2

AID

ELISA

001

0012

000

40013

001

133629

LPLR

FOryctolagus

cuniculus

Gallusgallus

AID

ACAb

B133630

LPPG

LHV

FPLA

SNRS

Mus

musculus

MD

AID

SPRminus001minus0013minus0021minus001minus001

96215

EEEE

AE

DKE

DHom

osapiens

Hom

osapiens

OAID

ELISA

96216

EEEG

LLK

KSADTL

WNMQK

Mus

musculus

Mus

musculus

AID

ELISA

008

0082

0078

0087

008

39782

LTAASV

Hom

osapiens

Triticum

aestivum

OOA

ELISA

39788

LTAEL

KIYS

VIQ

AEI

NKH

LOryctolagus

cuniculus

Yersiniapestis

AID

ACAb

B001

0014minus0001

0007

000

9

107479

KFNWYV

DHom

osapiens

Hom

osapiens

OAID

ELISA

107482

KGEP

GL

PGRG

FPGFP

Mus

musculus

Hom

osapiens

AID

ACAb

B0

0002

00007minus0001

63569

TETV

NSD

IMacaca

mulatta

Shigellaflexneri

AID

ELISA

63573

TEVEL

KER

KHRIED

AVRN

AK

Hom

osapiens

Mycobacteriu

mlep

rae

OOID

ELISA

005

0036

004

10049

005

134028

DDTIS

Mus

musculus

Hom

osapiens

AID

NIAA

134029

DED

ENQS

PRSFQKK

TROryctolagus

cuniculus

Hom

osapiens

AID

ELISA

005

0053

005

005

004

8

23028

GVKY

AHom

osapiens

MD

OAID

WI

23032

GVLA

KDV

RFSQ

VHom

osapiens

MD

EWEIR

ACAb

B0

00

000

4minus0003

115293

IMCV

KKILDK

Hom

osapiens

Bostaurus

DEW

EDWI

115295

INPS

KEN

LCST

FCK

EVVRN

AHom

osapiens

Bostaurus

OOA

FIAminus003minus003minus003minus002minus0029

65105

TLTP

ENTL

Mus

musculus

Shigellaflexneri

AID

ELISA

65110

TLTS

GSD

LDRC

TTFD

DV

Oryctolagus

cuniculus

SARS

coronavirus

AID

ELISA

001

0013minus0003

001

001

1344

71PK

PEQ

Mus

musculus

Streptococcus

pneumoniae

AID

FACS

1344

72PL

LPGT

STTS

TGPC

KTHom

osapiens

HepatitisB

virus

AID

ELISA

002

0018

0019

002

0016

Journal of Immunology Research 11Ta

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

142228

LYCY

EQLN

DSSE

Hom

osapiens

Hum

anpapillomaviru

sNI

ELISA

142250

NWGDEP

SKRR

DRS

NSR

GRK

NFelis

catus

Felin

einfectio

usAID

ELISA

007

006

80071

0073

007

21549

GNYD

FWYQ

SHom

osapiens

Staphylococcus

aureus

OOID

ELISA

21553

GNYN

YKY

RYLR

HGK

LRPF

ERMus

musculus

SARS

coronavirus

AID

ELISA

003

0032

0021

0031

003

21549

GNYD

FWYQ

SHom

osapiens

Staphylococcus

aureus

OOID

ELISA

21553

GNYN

YKY

RYLR

HGK

LRPF

EROryctolagus

cuniculus

SARS

coronavirus

AID

ELISA

003

0034

0021

0031

003

34908

LAPL

GE

Hom

osapiens

HepatitisE

virus

EWEIR

ELISA

34914

LAPS

TLRS

LRKR

RLSSP

Hom

osapiens

Hum

anherpesvirus

OOID

ELISA

006

006

0059

0063

006

130454

CLFP

NNSY

CMus

musculus

MD

AID

NIAA

130456

CRPQ

VN

NPK

EWS

CAAC

Hom

osapiens

MD

OOD

ACAb

B001

0008

001

0011

0007

1964

4GFV

PSM

Hom

osapiens

Hepatitisd

elta

virus

OOID

ELISA

1964

7GFV

SASI

FGFQ

AEV

GPN

NTR

Oryctolagus

cuniculus

VacciniavirusW

RAID

ELISAminus001minus000

6minus0013minus000

9minus001

20678

GKRP

EMus

musculus

Streptococcus

pyogenes

AID

WI

20680

GKS

KRD

AKN

NAAK

LAVDKL

LMus

musculus

VacciniavirusW

RAID

WI

001

001

0001

001

001

123278

GYL

KDLP

TTOvisa

ries

Fasciolahepatica

AID

ELISA

123282

HAC

QKK

LLKF

EAL

QQEE

GEE

Rattu

snorvegicu

sGallusgallus

AID

PFFminus002minus0016minus0016minus002minus0025

141067

PLSLEP

DP

Mus

musculus

Hom

osapiens

AID

FACS

141073

REGVRW

RVMAIQ

Mus

musculus

Hom

osapiens

AID

ELISA

006

006

006

006

0056

139248

SFAG

TVIE

Mus

musculus

Classicalsw

ine

AID

WI

139305

TAAQ

ITQ

RKWEA

ARE

AEQ

RROryctolagus

cuniculus

Hom

osapiens

AID

IP003

0033

0026

003

003

104515

HDCR

PKKI

Mus

musculus

LaCrossevirus

AID

IFAIH

104520

IAKE

QE

NKE

TIGT

LKKI

LDE

Rattu

snorvegicu

sStreptococcus

pyogenes

AID

ELISAminus006minus006minus0073minus006minus0061

113517

HLY

ADGLT

DMus

musculus

Hum

anpapillomaviru

sAID

IFAIH

113518

HNKI

QA

IELE

DLL

RYS

KLYR

Mus

musculus

Hom

osapiens

AID

ELISA

002

002

0011

002

0019

156970

VER

HQ

Hom

osapiens

Hom

osapiens

OAID

ELISA

156975

WSSTV

LRVS

PTRT

VP

Mus

musculus

MD

AID

ELISA

001

0012

0003

0017

001

78252

PVQNLT

Mus

musculus

Porphyromonas

gingivalis

AID

WI

78253

QGGCG

RGWAFS

ATG

AIEA

Mus

musculus

Glycinem

axAID

ELISA

002

002

0017

002

0018

6068

CCPD

KNKS

Mus

musculus

Hum

anherpesvirus

AID

WI

6074

CCRH

KQKD

VGDVK

QTL

PPS

Ovisa

ries

MD

AID

ELISA

001

000

6000

4001

0008

12 Journal of Immunology Research

Table3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

53109

RAGVC

YHom

osapiens

Triticum

aestivum

OOA

ELISA

53116

RAILTA

FSPA

QDI

WGTS

Oryctolagus

cuniculus

SARS

coronavirus

AID

ELISAminus002minus0016minus0037minus0023minus002

147041

IPEQ

Hom

osapiens

Triticum

aestivum

OOA

WI

147064

KHQGA

QYV

WN

RTA

Hom

osapiens

Bostaurus

OOID

WI

006

006

0047

0057

006

7066

4VQ

VVYD

YQOryctolagus

cuniculus

Treponem

apallidu

mAID

ELISA

70667

VQWMN

RLIAFA

FAG

NHVS

PHom

osapiens

HepatitisC

virus

OOID

ELISA

005

004

6004

10049

005

134343

DLY

IKMus

musculus

Hum

anpapillomaviru

sAID

PhDIP

134344

DMAQ

VTV

GPG

LLGVS

TLMus

musculus

Hom

osapiens

AID

PhDIP

00minus000

90

0

Journal of Immunology Research 13

The first input term is the value 120582(120576119894119895)ref is the scoring

function 120582 of the efficiency of the initial process 120576119894119895(known

solution) The function 120582(120576119894119895)ref = 1 if the 119894th peptide could

experimentally be demonstrated to be a B-epitope in theassay of reference (reference) carried out in the conditions119888119895 120582(120576119894119895)ref = 0 otherwise The variational-perturbation

terms ΔΔ120594119888119895are at the same time terms typical of pertur-

bation theory and moving average (MA) functions used inBox-Jenkin models in time series [60] These new types ofterms account both for the deviation of the electronegativityof all amino acids in the sequence of the new peptidewith respect to the peptide of reference and with respectto all boundary conditions In Table 1 we give the overallclassification results obtainedwith thismodel Speck-Plancheet al [61ndash63] introduced different multitargetmultiplexingQSARmodels that incorporate this type of information basedon MAs The results obtained with the present model areexcellent comparedwith other similarmodels in the literatureuseful for other problems including moving average models[64 65] or perturbation models [58] Notably this is also thefirst model combining both perturbation theory and MAs ina QSPR context

The other input terms are the following The first Δ120594seq =120594(119898119902)ref minus120594(119898119894)new is the perturbation term for the variation

or in themean value of electronegativity for all amino acids inthe sequence of the peptide of reference The remnant inputvariables of the model ΔΔ120594

119888119895= Δ120594

119888119895-ref minus Δ120594119888119895-new =

[120594(119898119902)ref minus

lowast120594 (119888119902119903)ref] minus [120594(119898119894)new minus

lowast120594 (119888119894119895)new] quantify

values of the conditions of the new assay 119888119895-new that representperturbations with respect to the initial conditions 119888

119894119895-ref of

the assay of reference The quantities lowast120594(119888119894119895) and lowast120594(119888

119902119903) are

the average values of the mean electronegativity values 120594(119898119894)

and 120594(119898119902) for all new and reference peptides in IEDB that

are epitopes under the 119895th or 119903th boundary condition Thevalues of these terms have been tabulated for gt500 sourceorganisms gt50 host organisms gt10 biological process andgt30 experimental techniques We must substitute the valuesof 120594(119898

119894) and 120594(119898

119902) of the new and reference peptides and the

tabulated values of lowast120594(119888119894119895) and lowast120594(119888

119902119903) for all combinations

of boundary conditions to predict the perturbations of theaction as epitope of peptides In doing so we can found theoptimal sequence and boundary conditions towards the useof the peptide in the development of a vaccine In Table 2 wegive some of these values of lowast120594(119888

119894119895) and lowast120594(119888

119902119903)

In Table 3 we depict the sequences and input-outputboundary conditions for top perturbations present in IEDBAll these perturbations have observed value of 120582(120576

119894119895)new = 1

and predicted value also equal to 1 with a high probabilitySee Supplementary Material available online at httpdxdoiorg1011552014768515 file contains a full list of gt200000cases of perturbations

4 Conclusions

It is possible to develop generalmodels for vaccine design ableto predict the results of multiple input-output perturbations

in peptide sequence and experimental assay boundary condi-tions using ideas of QSPR analysis perturbation theory andBox and Jenkins MA operators The electronegativity valuescalculated withMARCH-INSIDE seem to be goodmoleculardescriptors for this type of QSPR-perturbation models

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

The present study was partially supported by GrantsAGL2010-22290-C02 and AGL2011-30563-C03 from Minis-terio de Ciencia e Innovacion Spain and Grant CN 2012155from Xunta de Galicia Spain

References

[1] B Peters J Sidney P Bourne et al ldquoThe design and implemen-tation of the immune epitope database and analysis resourcerdquoImmunogenetics vol 57 no 5 pp 326ndash336 2005

[2] B Peters J Sidney P Bourne et al ldquoThe immune epitopedatabase and analysis resource from vision to blueprintrdquo PLoSBiology vol 3 no 3 p e91 2005

[3] PWang A AMorgan Q Zhang A Sette and B Peters ldquoAuto-mating document classification for the Immune Epitope Data-baserdquo BMC Bioinformatics vol 8 article 269 2007

[4] A Sette ldquoThe immune epitope database and analysis resourcefrom vision to blueprintrdquo Genome Informatics vol 15 no 2 p299 2004

[5] N Salimi W Fleri B Peters and A Sette ldquoThe immune epi-tope database a historical retrospective of the first decaderdquoImmunology vol 137 no 2 pp 117ndash123 2012

[6] Y Kim A Sette and B Peters ldquoApplications for T-cell epitopequeries and tools in the Immune EpitopeDatabase andAnalysisResourcerdquo Journal of Immunological Methods vol 374 no 1-2pp 62ndash69 2011

[7] Y Kim J Ponomarenko Z Zhu D Tamang et al ldquoImmuneepitope database analysis resourcerdquo Nucleic Acids Research ppW525ndashW530 2012

[8] A M Helguera R D Combes M P Gonzalez and M N DS Cordeiro ldquoApplications of 2D descriptors in drug design aDRAGON talerdquo Current Topics in Medicinal Chemistry vol 8no 18 pp 1628ndash1655 2008

[9] GM Casanola-Martın Y Marrero-Ponce M T H Khan et alldquoDragon method for finding novel tyrosinase inhibitors Biosil-ico identification and experimental in vitro assaysrdquo The Euro-pean Journal of Medicinal Chemistry vol 42 no 11-12 pp 1370ndash1381 2007

[10] I V Tetko J Gasteiger R Todeschini et al ldquoVirtual computa-tional chemistry laboratorymdashdesign and descriptionrdquo Journalof Computer-AidedMolecular Design vol 19 no 6 pp 453ndash4632005

[11] A R Katritzky A Oliferenko A Lomaka and M KarelsonldquoSix-membered cyclic ureas as HIV-1 protease inhibitors aQSAR study based on CODESSA PRO approachrdquo Bioorganicand Medicinal Chemistry Letters vol 12 no 23 pp 3453ndash34572002

14 Journal of Immunology Research

[12] A R Katritzky S Perumal R Petrukhin and E Klein-peter ldquoCODESSA-based theoretical QSPRmodel for hydantoinHPLC-RT lipophilicitiesrdquo Journal of Chemical Information andComputer Sciences vol 41 no 3 pp 569ndash574 2001

[13] S Vilar G Cozza and S Moro ldquoMedicinal chemistry and themolecular operating environment (MOE) application of QSARand molecular docking to drug discoveryrdquo Current Topics inMedicinal Chemistry vol 8 no 18 pp 1555ndash1572 2008

[14] G Marzaro A Chilin A Guiotto et al ldquoUsing the TOPS-MODE approach to fit multi-target QSAR models for tyrosinekinases inhibitorsrdquo The European Journal of Medicinal Chem-istry vol 46 no 6 pp 2185ndash2192 2011

[15] S Vilar E Estrada E Uriarte L Santana and Y Gutierrez ldquoInsilico studies toward the discovery of new anti-HIV nucleosidecompounds through the use of tops-mode and 2D3D con-nectivity indices 2 Purine derivativesrdquo Journal of ChemicalInformation and Modeling vol 45 no 2 pp 502ndash514 2005

[16] E Estrada J A Quincoces and G Patlewicz ldquoCreating molec-ular diversity from antioxidants in Brazilian propolis combina-tion of TOPS-MODE QSAR and virtual structure generationrdquoMolecular Diversity vol 8 no 1 pp 21ndash33 2004

[17] E Estrada andHGonzalez ldquoWhat are the limits of applicabilityfor graph theoretic descriptors in QSPRQSAR modelingdipole moments of aromatic compounds with TOPS-MODEdescriptorsrdquo Journal of Chemical Information and ComputerSciences vol 43 no 1 pp 75ndash84 2003

[18] Y Marrero-Ponce J A Castillo-Garit E Olazabal et al ldquoTom-ocomd-Cardd a novel approach for computer-aided ldquorationalrdquodrug design I Theoretical and experimental assessment of apromising method for computational screening and in silicodesign of new anthelmintic compoundsrdquo Journal of Computer-Aided Molecular Design vol 18 no 10 pp 615ndash634 2004

[19] Y Marrero Ponce R Medina Marrero E A Castro et al ldquoPro-tein quadratic indices of the ldquomacromolecular pseudographrsquos 120572-carbon atom adjacency matrixrdquo 1 prediction of arc repressoralanine-mutantrsquos stabilityrdquoMolecules vol 9 no 12 pp 1124ndash11472004

[20] H Gonzalez-Dıaz F Prado-Prado and F M Ubeira ldquoPredict-ing antimicrobial drugs and targets with the MARCH-INSIDEapproachrdquo Current Topics in Medicinal Chemistry vol 8 no 18pp 1676ndash1690 2008

[21] R Garcıa-Domenech J Galvez J V de Julian-Ortiz and LPogliani ldquoSomenew trends in chemical graph theoryrdquoChemicalReviews vol 108 no 3 pp 1127ndash1169 2008

[22] E Estrada and E Uriarte ldquoRecent advances on the role of topo-logical indices in drug discovery researchrdquo Current MedicinalChemistry vol 8 no 13 pp 1573ndash1588 2001

[23] R Todeschini andVConsonniHandbook ofMolecularDescrip-tors Wiley-VCH Weinheim Germany 2008

[24] E Estrada E J Delgado J B Alderete and G A Jana ldquoQuan-tum-connectivity descriptors inmodeling solubility of environ-mentally important organic compoundsrdquo Journal of Computa-tional Chemistry vol 25 no 14 pp 1787ndash1796 2004

[25] E Besalu X Girones L Amat andR Carbo-Dorca ldquoMolecularquantum similarity and the fundamentals of QSARrdquo Accountsof Chemical Research vol 35 no 5 pp 289ndash295 2002

[26] D A Rincon M N D S Cordeiro and R A Mosquera ldquoOnthe electronic structure of cocaine and its metabolitesrdquo Journalof Physical Chemistry A vol 113 no 50 pp 13937ndash13942 2009

[27] M Mandado M J Gonzalez-Moa and R A MosqueraldquoChemical graph theory and n-center electron delocalization

indices a study on polycyclic aromatic hydrocarbonsrdquo Journalof Computational Chemistry vol 28 no 10 pp 1625ndash1633 2007

[28] THill andP Lewicki STATISTICSMethods andApplications AComprehensive Reference for Science Industry and DataMiningStatSoft Tulsa Okla USA 2006

[29] E Frank M Hall L Trigg G Holmes and I H Witten ldquoDatamining in bioinformatics using Wekardquo Bioinformatics vol 20no 15 pp 2479ndash2481 2004

[30] G Patlewicz N Ball E D Booth E Hulzebos E Zvinavasheand C Hennes ldquoUse of category approaches read-across and(Q)SAR general considerationsrdquo Regulatory Toxicology andPharmacology vol 67 no 1 pp 1ndash12 2013

[31] D W Roberts and G Y Patlewicz ldquoUpdating the skin sensiti-zation in vitro data assessment paradigm in 2009mdasha chemistryand QSAR perspectiverdquo Journal of Applied Toxicology vol 30no 3 pp 286ndash288 2010

[32] D W Roberts and G Y Patlewicz ldquoNonanimal alternatives forskin sensitization letter to the editorrdquoToxicological Sciences vol106 no 2 pp 572ndash574 2008

[33] E Estrada G Patlewicz and Y Gutierrez ldquoFrom knowledgegeneration to knowledge archive a general strategy usingTOPS-MODE with DEREK to formulate new alerts for skinsensitizationrdquo Journal of Chemical Information and ComputerSciences vol 44 no 2 pp 688ndash698 2004

[34] G F Gerberick C A Ryan P S Kern et al ldquoA chemical datasetfor evaluation of alternative approaches to skin-sensitizationtestingrdquo Contact Dermatitis vol 50 no 5 pp 274ndash288 2004

[35] G Y Patlewicz D A Basketter C K Smith Pease et al ldquoFurtherevaluation of quantitative structurendashactivity relationship mod-els for the prediction of the skin sensitization potency ofselected fragrance allergensrdquo Contact Dermatitis vol 50 no 2pp 91ndash97 2004

[36] E Estrada G Patlewicz M Chamberlain D Basketter andS Larbey ldquoComputer-aided knowledge generation for under-standing skin sensitization mechanisms the TOPS-MODEapproachrdquo Chemical Research in Toxicology vol 16 no 10 pp1226ndash1235 2003

[37] G Patlewicz R Rodford and J D Walker ldquoQuantitative struc-ture-activity relationships for predicting skin and eye irritationrdquoEnvironmental Toxicology and Chemistry vol 22 no 8 pp1862ndash1869 2003

[38] G Y Patlewicz R A Rodford G Ellis and M D Barratt ldquoAQSAR model for the eye irritation of cationic surfactantsrdquo Tox-icology In Vitro vol 14 no 1 pp 79ndash84 2000

[39] E Tenorio-Borroto C G Penuelas Rivas J C VasquezChagoyan N Castanedo F J Prado-Prado and X Garcia-Mera ldquoANN multiplexing model of drugs effect on macro-phages theoretical and flow cytometry study on the cyto-toxicity of the anti-microbial drug G1 in spleenrdquo Bioorganic ampMedicinal Chemistry vol 20 no 20 pp 6181ndash6194 2012

[40] D R Flower H McSparron M J Blythe et al ldquoComputationalvaccinology quantitative approachesrdquo Novartis FoundationSymposium vol 254 pp 102ndash120 2003

[41] I A Doytchinova P Guan and D R Flower ldquoQuantitativestructure-activity relationships and the prediction of MHCsupermotifsrdquoMethods vol 34 no 4 pp 444ndash453 2004

[42] Y Xiao and M R Segal ldquoPrediction of genomewide conservedepitope profiles of HIV-1 classifier choice and peptide rep-resentationrdquo Statistical Applications in Genetics and MolecularBiology vol 4 no 1 article 25 2005

Journal of Immunology Research 15

[43] T Fagerberg V Zoete S Viatte P Baumgaertner P M Alves PRomero et al ldquoPrediction of cross-recognition of peptide-HLAA2 by melan-a-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationshipsrdquo PLoSONE vol 8 no 7 Article ID e65590 2013

[44] D Barh A N Misra A Kumar and A Vasco ldquoA novel strategyof epitope design inNeisseria gonorrhoeaerdquo Bioinformation vol5 no 2 pp 77ndash82 2010

[45] J Bi R Song H Yang et al ldquoStepwise identification of HLA-Alowast0201-restricted CD8+ T-cell epitope peptides from herpessimplex virus type 1 genome boosted by a steprank schemerdquoBiopolymers vol 96 no 3 pp 328ndash339 2011

[46] R D Bremel and E J Homan ldquoAn integrated approach to epit-ope analysis II a system for proteomic-scale prediction ofimmunological characteristicsrdquo Immunome Research vol 6 no1 article 8 2010

[47] C M Diez-Rivero B Chenlo P Zuluaga and P A RecheldquoQuantitative modeling of peptide binding to TAP using sup-port vector machinerdquo Proteins vol 78 no 1 pp 63ndash72 2010

[48] E Martınez-Naves E M Lafuente and P A Reche ldquoRecogni-tion of the ligand-type specificity of classical and non-classicalMHC I proteinsrdquo FEBS Letters vol 585 no 21 pp 3478ndash34842011

[49] M Bhasin E L Reinherz and P A Reche ldquoRecognition andclassification of histones using support vectormachinerdquo Journalof Computational Biology vol 13 no 1 pp 102ndash112 2006

[50] P A Reche and E L Reinherz ldquoPEPVAC a web server formulti-epitope vaccine development based on the prediction ofsupertypic MHC ligandsrdquo Nucleic Acids Research vol 33 no 2pp W138ndashW142 2005

[51] E M Lafuente and P A Reche ldquoPrediction of MHC-peptidebinding a systematic and comprehensive overviewrdquo CurrentPharmaceutical Design vol 15 no 28 pp 3209ndash3220 2009

[52] A Gaulton L J Bellis A P Bento et al et al ldquoA large-scalebioactivity database for drug discoveryrdquoNucleic Acids Researchpp D1100ndashD1107 2012

[53] W H Cropper Great Physicists The Life and Times of LeadingPhysicists from Galileo to Hawking Oxford University PressNew York NY USA 2004

[54] E L Bouzarth A Brooks R Camassa et al ldquoEpicyclic orbits ina viscous fluid about a precessing rod theory and experimentsat the micro-and macro-scalesrdquo Physical Review E vol 76 no1 part 2 Article ID 016313 2007

[55] M Laberge ldquoIntrinsic protein electric fields basic non-covalentinteractions and relationship to protein-induced Stark effectsrdquoBiochimica et BiophysicaActa vol 1386 no 2 pp 305ndash330 1998

[56] FM Fernandez and J AMorales ldquoPerturbation theorywithoutwave functions for the Zeeman effect in hydrogenrdquo PhysicalReview A vol 46 no 1 pp 318ndash326 1992

[57] B D Marshall and W G Chapman ldquoThermodynamic pertur-bation theory for associating fluids with small bond angleseffects of steric hindrance ring formation and double bondingrdquoPhysical Reiew E vol 87 no 5 Article ID 052307 12 pages 2013

[58] H Gonzalez-Diaz S Arrasate A Gomez-San Juan et al ldquoNewtheory for multiple input-output perturbations in complexmolecular systems 1 Linear QSPR electronegativity models inphysical organic and medicinal chemistryrdquo Current Topics inMedicinal Chemistry 2013

[59] H Gonzalez-Diaz S Arrasate N Sotomayor et al ldquoMIANNmodels in medicinal physical and organic chemistryrdquo CurrentTopics in Medicinal Chemistry vol 13 no 5 pp 619ndash641 2013

[60] G E P Box and GM Jenkins Time Series Analysis Forecastingand Control Holden-Day San Francisco Calif USA 1970

[61] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoUnified multi-target approach for the rational insilico design of anti-bladder cancer agentsrdquo Anti-Cancer Agentsin Medicial Chemistry vol 13 no 5 pp 791ndash800 2013

[62] A Speck-Planche V V Kleandrova and M N Cordeiro ldquoNewinsights toward the discovery of antibacterial agents multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugsrdquo TheEuropean Journal of Pharmaceutical Sciences vol 48 no 4-5pp 812ndash818 2013

[63] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoMulti-target inhibitors for proteins associated withAlzheimer in silico discovery using fragment-based descrip-torsrdquo Current Alzheimer Research vol 10 no 2 pp 117ndash1242013

[64] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoIn silico discovery and virtual screening of multi-target inhibitors for proteins in Mycobacterium tuberculosisrdquoCombinatorial Chemistry amp High Throughput Screening vol 15no 8 pp 666ndash673 2012

[65] A Speck-Planche V V Kleandrova F Luan and M N DS Cordeiro ldquoChemoinformatics in anti-cancer chemotherapymulti-target QSAR model for the in silico discovery of anti-breast cancer agentsrdquo The European Journal of PharmaceuticalSciences vol 47 no 1 pp 273ndash279 2012

Submit your manuscripts athttpwwwhindawicom

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Behavioural Neurology

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Disease Markers

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BioMed Research International

OncologyJournal of

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Oxidative Medicine and Cellular Longevity

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PPAR Research

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Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Computational and Mathematical Methods in Medicine

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Research and TreatmentAIDS

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Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Page 4: Research Article Model for Vaccine Design by …downloads.hindawi.com/journals/jir/2014/768515.pdfResearch Article Model for Vaccine Design by Prediction of B-Epitopes of IEDB Given

4 Journal of Immunology Research

Table 2 Average values and count of input-output cases for differentorganisms process and techniques

Source organism (so) 119873in 119873outlowast120594

Homo sapiens 38920 39274 2685Plasmodium falciparum 10669 9446 2704Hepatitis C virus 9935 10239 2683Bos taurus 5671 5780 2690Canine parvovirus 5655 5637 2693Foot-mouth disease virus 4062 4176 2676Triticum aestivum 3769 3887 2703Bacillus anthracis 3602 3600 2699Human papillomavirus 3316 3414 2693Human herpesvirus 3026 3132 2684Gallus gallus 2850 2829 2689Arachis hypogaea 2648 2670 2687Mycobacterium tuberculosis 2637 2593 2688Clostridium botulinum 2588 2722 2685SARS coronavirus 2550 2704 2686Mus musculus 2334 2287 2682Hepatitis B virus 2007 2066 2680Helicobacter pylori 1958 1796 2695Hevea brasiliensis 1938 1958 2697Hepatitis E virus 1928 1941 2685Shigella flexneri 1878 1701 2699Dengue virus 2 1767 1828 2679Staphylococcus aureus 1757 1661 2694Treponema pallidum 1739 1755 2691Escherichia coli 1721 1678 2689Murine hepatitis virus 1575 1603 2692Haemophilus influenzae 1545 1587 2695Streptococcus mutans 1523 1537 2697Puumala virus (strain) 1505 1574 2689Chlamydia trachomatis 1402 1546 2704Human respiratory virus 1347 1398 2682Borrelia burgdorferi 1228 1237 2698Hepatitis delta virus 1182 1199 2690Streptococcus pyogenes 1181 1251 2697Porphyromonas gingivalis 1143 1085 2688Human enterovirus 1106 1132 2689Influenza A virus 1085 1086 2687Mycoplasma hyopneumoniae 1044 1024 2695Rattus norvegicus 1025 1039 2689Bordetella pertussis 1011 960 2685Human T-lymphotropic virus 996 1031 2680Anaplasma marginale 977 857 2707Measles virus strain 804 810 2688Fasciola hepatica 803 857 2685Neisseria meningitidis 789 853 2696Human poliovirus 766 780 2690Tityus serrulatus 764 775 2680Torpedo californica 752 788 2687

Table 2 Continued

Source organism (so) 119873in 119873outlowast120594

Cryptomeria japonica 719 794 2680Mycobacterium bovis 717 733 2688Trypanosoma cruzi 691 777 2704Andes virus CHI-7913 679 687 2690Bovine papillomavirus 672 665 2692Human hepatitis 670 696 2688Leishmania infantum 659 735 2688Human parvovirus 649 691 2683Poa pratensis 648 664 2692Aspergillus fumigatus 642 709 2677Duck hepatitis 587 603 2688Olea europaea 571 577 2692Porcine reproductive 515 514 2681Fagopyrum esculentum 509 497 2685Juniperus ashei 505 568 2672Mycobacterium leprae 489 542 2690Glycine max 477 509 2685D pteronyssinus 455 464 2680Plasmodium vivax 453 446 2690Chlamydophila pneumoniae 446 462 2690Pseudomonas aeruginosa 443 454 2691Vibrio cholera 427 426 2694Streptococcus sp 426 425 2691Mycobacterium avium 425 415 2689Dermatophagoides farinae 410 390 2693Human coxsackievirus 406 392 2694Equine infectious virus 404 419 2688Babesia equi 383 371 2696Prunus dulcis 383 379 2708Human adenovirus 375 405 2686Theileria parva 366 371 2713Candida albicans 365 370 2690Porcine endogenous 355 351 2692Ovis aries 352 350 2683Chironomus thummi 347 338 2691Sus scrofa 343 362 2686Bovine leukemia virus 333 329 2676Ricinus communis 329 314 2692Androctonus australis 322 357 2685Renibacterium salmoninarum 319 350 2690Orientia tsutsugamushi 309 372 2705Anacardium occidentale 293 306 2693Conus geographus 289 295 2660Host organism (ho) 119873in 119873out

lowast120594

Homo sapiens 257293 91093 26856Mus musculus 107867 51466 26873Oryctolagus cuniculus 65053 31433 26900Bos taurus 15333 2072 26909Rattus norvegicus 9450 3562 26876Aotus sp 9044 3933 26879Sus scrofa 7725 3464 26873Gallus gallus 7507 997 26790

Journal of Immunology Research 5

Table 2 Continued

Source organism (so) 119873in 119873outlowast120594

Canis lupus 6604 3334 26906Macaca mulatta 5261 2569 26993Ovis aries 3953 1653 26836Equus caballus 3943 2099 26842Cavia porcellus 3458 1688 26833Capra hircus 2182 1127 26830Aotus nancymaae 1659 852 26837Pan troglodytes 1614 732 26757Marmota monax 1100 509 27011Felis catus 901 279 26838Myodes glareolus 814 388 26863Anas platyrhynchos 688 342 26880Homo sapiens (human) 508 270 26851Trichosurus vulpecula 456 126 26921Mesocricetus auratus 438 104 26909Macaca cyclopis 382 193 26871O tshawytscha 333 159 26929Macaca fuscata 188 100 26667Cricetulus migratorius 171 142 27008Camelus dromedarius 171 89 26886Dicentrarchus labrax 121 55 26759Macaca fascicularis 96 52 26793Saimiri sciureus 92 44 26900Canis familiaris 77 42 26850Rattus rattus 72 31 26760Callithrix pygmaea 67 30 26920Chinchilla lanigera 41 24 26729Aotus lemurinus 30 19 26860Papio cynocephalus 27 13 27267Aotus griseimembra 26 12 27000Mustela vison 18 10 27000Chlorocebus aethiops 15 10 26875Bos indicus 13 4 26925Oncorhynchus mykiss 10 4 26700M macquariensis 9 6 26600Cricetulus griseus 8 4 26900Aotus trivirgatus 7 4 27000Process type (pt) 119873in 119873out

lowast120594

AID 111197 108536 26876OOID 32419 32617 26868OAID 19210 18954 26801OOA 15863 16303 26902NI 13430 15206 26845EWEIR 4818 4864 26843EEE 3113 3546 26906OOD 2806 2799 26887AICD 1077 1095 26812EWED 696 686 26879DEWED 280 337 26804TT 260 215 26806OOC 153 137 26800

Table 2 Continued

Source organism (so) 119873in 119873outlowast120594

Technique (tq) 119873in 119873outlowast120594

ELISA 133458 135109 26871WI 33627 33292 26887ACAbB 7780 9068 26862PhDIP 7450 4496 26771RIA 5241 5218 26858IFAIH 4454 4581 26879NIAA 4222 4316 26892FIA 2255 2276 26897PAC 1312 1219 26837IP 1127 1089 26886SPR 758 639 26860FACS 608 647 26907Other 502 495 26813SAC 484 393 26878ELISPOT 396 412 26979RDAT 366 323 26859EDAT 284 330 26800XRC 231 227 26880MS 209 179 26849PFF 171 153 26820AbDPO 162 295 26968CdC 146 205 26895IAbBA 144 183 26940IOT 124 106 26835HAGGI 115 122 26834IgMHR 89 90 26929EAAA 84 139 26922HS 82 67 26791AbdCC 73 118 26897AGG 50 60 26980CM 50 57 26863The lowastindicates that quantities like lowast120594 is the average value of the meanelectronegativity (119898119894) for all the peptides in IEDB that are epitopes for thesame boundary condition

3 Results and Discussion

We propose herein for the first time a QSRR-perturbationmodel able to predict variations in the propensity of a peptideto act as B-epitope taking into consideration the propensityof a peptide of reference and the changes in peptide sequenceimmunological process host organism source organismsand the experimental technique used The best QSPR-perturbation model found here with LDA was

120582(120576119894119895)new

= 4979 sdot 120582(120576119894119895)refminus 221642 sdot Δ120594seq

+ 8770 sdot ΔΔ120594ho + 63572 sdot ΔΔ120594so

minus 55387 sdot ΔΔ120594ip + 201919 sdot ΔΔ120594tq minus 2149

119873 = 155169 119877119888 = 092

119880 = 015 119901 lt 001

(6)

6 Journal of Immunology Research

Table3To

p100

values

ofp1

forp

ositive

perturbatio

nsin

training

serie

sNew

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

115153

MKG

VVC

TRIYEK

VHom

osapiens

Hom

osapiens

OAID

ELISA

115155

NNQRK

KAKN

TPFN

MLK

RERN

Mus

musculus

Denguev

irus2

AID

WI

001

0012

000

40017

0012

52124

QQQPP

Hom

osapiens

Triticum

aestivum

OOA

ELISA

52128

QQQQGG

SQSQ

KGKQ

QHom

osapiens

Glycinem

axOOA

WI

00minus0018

00002

3639

APL

GVT

Hom

osapiens

HepatitisE

virus

EWEIR

ELISA

3652

APL

TRG

SCRK

RNRS

PER

Hom

osapiens

Hum

anherpesvirus

OOID

ELISA

004

004

0039

0043

004

135959

LTRA

YAKD

VKF

GHom

osapiens

Hom

osapiens

OAID

ELISA

135963

NGQEE

KAG

VVS

TGLIGGG

Mus

musculus

MD

AID

ELISAminus004minus0038minus0047minus0033minus004

108075

PREP

QVY

Hom

osapiens

Hom

osapiens

OAID

ELISA

108078

PTSP

SGV

EEWIV

TQVVPG

VAOryctolagus

cuniculus

Hom

osapiens

AID

ACAb

Bminus001minus000

6minus001minus0003minus0011

25113

HVVDLP

Hom

osapiens

HepatitisE

virus

EWEIR

ELISA

25126

HWGNH

SKSH

PQR

Mus

musculus

MD

AID

ELISA

002

0022

0013

0023

002

48780

PPFSPQ

Hom

osapiens

Hepatitisd

elta

virus

OOID

ELISA

48782

PPFT

SAV

GGVDHRS

Mus

musculus

MD

AID

SAC

002

0022

0008

0021

0021

40988

LYVVA

YQA

Mus

musculus

Viscum

album

AID

ELISA

4100

4MAARL

CCQLD

PARD

VHom

osapiens

HepatitisB

virus

OOID

ELISA

002

0018

000

60019

002

50439

QDAY

NAAG

Mus

musculus

Mycobacteriu

mscrofulaceum

AID

ELISA

5044

5QDCN

CSI

YPGHAS

GHRM

AWD

Hom

osapiens

HepatitisC

virus

OOID

ELISA

004

0038

0028

0039

004

98849

KIPA

VFK

IDA

Hom

osapiens

Bostaurus

DEW

EDWI

98850

KKGSE

EEGDITNPIN

Hom

osapiens

Arachish

ypogaea

OOA

IFAIHminus005minus005minus0053minus004minus0051

116171

TQDQDP

BBHFF

KNIV

TPR

Hom

osapiens

Hom

osapiens

OAID

ACAb

B116

286

CGKG

LSAT

VTG

GQKG

RGSR

Oryctolagus

cuniculus

Mus

musculus

AID

MS

001

0014

0008

0017

000

9

123442

LLKD

LRKN

Hom

osapiens

Bornadiseasev

irus

EWEIR

WI

123443

LLTE

HR

MTW

DPA

QPP

RDLT

EHom

osapiens

Hom

osapiens

OOID

ELISAminus002minus002minus0025minus0017minus0022

47858

PHVVDL

Hom

osapiens

HepatitisE

virus

EWEIR

ELISA

47860

PHWIK

KPNRQ

GLG

YYS

Caprahircus

Hum

anT-lymphotropic

virus

AID

ACAb

B001

0007

0005

0013

000

9

61783

STNKA

VVS

LSBo

staurus

Bovine

respira

tory

AID

ELISA

61792

STNPK

PQRK

TKRN

TNRR

PQD

Hom

osapiens

HepatitisC

virus

OOID

ELISA

003

0025

0019

0029

003

118210

VMLY

QISEE

Hom

osapiens

Hom

osapiens

OAID

WI

118217

VTK

YITK

GWKE

VH

Oryctolagus

cuniculus

Hom

osapiens

AID

ELISA

005

0054

005

0057

004

8

130944

LFKH

SOryctolagus

cuniculus

Rattu

snorvegicus

AID

ELISA

130956

LPPR

VTP

KWSLDA

WST

WR

Hom

osapiens

MD

OOD

WI

001

000

6minus0002

0011

0012

Journal of Immunology Research 7Ta

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

23028

GVKY

AHom

osapiens

MD

OAID

WI

23032

GVLA

KDVRF

SQV

Hom

osapiens

MD

OOID

ELISA

00

00007minus0002

51199

QKK

AIE

Oryctolagus

cuniculus

Vibriocholerae

AID

ELISA

51204

QKK

NK

RNTN

RRPQ

DV

Hom

osapiens

HepatitisC

virus

OOID

ELISA

003

0026

0018

0029

003

144783

SHVVT

MLD

NF

Hom

osapiens

Hom

osapiens

NI

ELISA

144786

SMNRG

RGTH

PSLIWM

Mus

musculus

MD

AID

ACAb

B003

0032

0023

0033

0029

134343

DLY

IKMus

musculus

Hum

anpapillomaviru

sAID

NIAA

134344

DMAQ

VTV

GPG

LLGVS

TLMus

musculus

Hom

osapiens

AID

WI

00minus000

90

0

38321

LNQLA

GRM

Anas

platyrhynchos

Duckhepatitis

AID

ELISA

38323

LNQTA

RAFP

DCA

ICW

EPSP

POryctolagus

cuniculus

Bovine

leukemia

virus

AID

ACAb

Bminus001minus0008minus0022minus001minus0011

144657

GQITVD

MMYG

Hom

osapiens

Hom

osapiens

OAID

ELISA

1446

61GRE

GYP

ADGGCA

WPA

CYC

Oryctolagus

cuniculus

MD

AID

WI

002

0024

0013

0027

0022

21084

GLQ

NMus

musculus

Chlamydia

trachom

atis

AID

ELISA

21093

GLR

AQD

DFS

GWDI

NTP

AFE

WMus

musculus

Mycobacteriu

mtuberculosis

AID

WI

003

003

0013

003

0032

98453

SGFS

GSV

QFV

Oryctolagus

cuniculus

Neisseria

meningitid

isAID

ELISA

98456

SICS

NN

PTCW

AIC

KRIPNKK

Mus

musculus

Hum

anrespira

tory

virus

AID

IFAIH

004

0037

0027

004

004

1

98453

SGFS

GSV

QFV

Mus

musculus

Neisseria

meningitid

isAID

ELISA

98456

SICS

NN

PTCW

AIC

KRIPNKK

Mus

musculus

Hum

anrespira

tory

virus

AID

IFAIH

004

004

0027

004

004

1

107107

EAIQ

PRa

ttus

norvegicu

sHom

osapiens

AID

ELISA

107110

EKER

RPSP

IGTA

TLL

Hom

osapiens

MD

OOA

ELISA

005

004

80043

0053

005

110857

FTGEA

YSY

WSA

KHom

osapiens

Mycoplasm

apenetra

nsEW

EIR

ELISA

110859

GEE

SRIS

LPLP

NF

SSLN

LRE

Mus

musculus

Hom

osapiens

AID

FIA

00002minus0017

0003

0003

36315

LGSA

YPMus

musculus

Mycobacteriu

mlep

rae

MD

ELISA

36317

LGSG

AFG

TIYK

GMus

musculus

Avian

erythroblasto

sisvirus

AID

ACAb

B001

001

00011

000

9

122034

WNPA

DRa

ttus

norvegicu

sTorpedocalifornica

AID

ELISA

122035

WNPA

DYG

GIK

WN

PADYG

GIK

Rattu

snorvegicu

sMD

AID

RIA

001

001

0001

001

000

9

25013

HVA

DID

KLID

Mus

musculus

Puum

alavirus

Kazan

AID

ELISA

25021

HVA

PTH

YVTE

SDA

SQRV

TQL

Hom

osapiens

HepatitisC

virus

OOID

ELISA

0minus0002minus0014minus0001

0

36162

LGIH

EOryctolagus

cuniculus

Cand

idaalbicans

AID

ELISA

36166

LGIM

GE

YRGTP

RNQDLY

DAA

Mus

musculus

Hum

anrespira

tory

virus

AID

RIA

0minus0003minus0007

0minus0001

67253

TWEV

LHMus

musculus

Plasmodium

vivax

AID

ELISA

67257

TWGEN

ETDVLL

LNNTR

PPQ

Hom

osapiens

HepatitisC

virus

OOID

ACAb

Bminus002minus0022minus0027minus0021minus0021

8 Journal of Immunology Research

Table3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

50990

QGYR

VSSY

LPHom

osapiens

Hevea

brasiliensis

OOA

WI

50998

QHEQ

DR

PTPS

PAP

SRPF

SVL

Hom

osapiens

HepatitisE

virus

OOID

ELISA

001

001minus0002

0007

0008

100458

RDVLQ

LYAPE

Mus

musculus

Bacillusa

nthracis

AID

ELISA

1004

62RF

STRY

GNQNGRI

RVLQ

RFD

Hom

osapiens

Arachish

ypogaea

EWED

ELISA

003

0028

0018

003

003

111036

TEST

FTGEA

YSV

Hom

osapiens

Mycoplasm

apenetra

nsEW

EIR

ELISA

111039

TGVPID

PAVPD

SSIV

PLLE

SBo

staurus

Bovine

papillomaviru

sAID

ELISA

003

0035

002

0033

003

117919

IFIEME

Hom

osapiens

Hom

osapiens

OAID

WI

117921

IGIIDLIE

KRKF

NQ

Mus

musculus

Hom

osapiens

AID

WI

003

0032

003

0037

003

7127

CTDTD

KLF

Oryctolagus

cuniculus

Shigellaflexneri

AID

ELISA

7128

CTDVS

TAIH

ADQL

TPAW

Hom

osapiens

SARS

coronavirus

OOID

ELISA

001

000

6minus0003

000

9001

112253

PGQSP

KLHom

osapiens

Hom

osapiens

OAID

ELISA

112255

PIRA

LVGDEV

ELP

CRISPG

KMus

musculus

Hom

osapiens

AID

ELISA

001

0012

001

0017

001

122034

WNPA

DRa

ttus

norvegicu

sTorpedocalifornica

AID

ELISA

122038

WNPD

DY

GGVKW

NP

DDYG

GVK

Rattu

snorvegicu

sMD

AID

RIA

00minus000

90minus0001

131878

FLMLV

GGST

LHom

osapiens

Hom

osapiens

OAID

ACAb

B131879

FLVA

HT

RARA

PSA

GER

ARR

SMus

musculus

Mus

musculus

AID

NIAA

003

0032

0028

0037

0033

7066

4VQ

VVYD

YQHom

osapiens

Treponem

apallidu

mOOID

ELISA

70667

VQWMNR

LIAFA

FAG

NHVS

PHom

osapiens

HepatitisC

virus

OOID

ELISA

005

005

004

1005

005

71545

VTV

Hom

osapiens

Helicobacterpylori

OOID

ELISA

71559

VTV

RGGL

RILSPD

RKHom

osapiens

Arachish

ypogaea

OOA

WI

004

004

0032

0043

004

2

127856

TDVRY

KD

Mus

musculus

Mus

musculus

AID

ACAb

B127857

TDVRY

KDDMYH

FFCP

AIQ

AQMus

musculus

Mus

musculus

AID

PFF

001

001

001

001

000

6

112149

GVG

WIRQ

Hom

osapiens

Hom

osapiens

OAID

ELISA

112152

HHPA

RTAHYG

SLP

QKS

HGRT

Hom

osapiens

Hom

osapiens

AID

ELISA

00

00007

0

119581

FSCS

VMHE

Hom

osapiens

Hom

osapiens

OAID

ELISA

119582

GLQ

LIQL

INVDEV

NQI

Mus

musculus

Hom

osapiens

AID

RIAminus001minus0008minus001minus0003minus0011

144657

GQITVD

MMYG

Hom

osapiens

Hom

osapiens

OAID

ELISA

144659

GRE

GYP

ADGGAA

GYC

NTE

Oryctolagus

cuniculus

MD

AID

WIminus001minus000

6minus0017minus0003minus0008

25013

HVA

DI

DKL

IDMus

musculus

Puum

alavirus

Kazan

AID

ELISA

25022

HVA

PTH

YVVES

DA

SQRV

TQV

Hom

osapiens

HepatitisC

virus

OOID

ELISA

0minus0002minus0014minus0001

0

Journal of Immunology Research 9Ta

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

144652

GMRG

MKG

LVY

Hom

osapiens

Hom

osapiens

OAID

ELISA

144654

GPH

PTLE

VVPM

GRG

SMus

musculus

MD

AID

ELISAminus002minus0018minus0027minus0013minus002

104515

HDCR

PKKI

Mus

musculus

LaCrossevirus

AID

IFAIH

104521

IGTL

KKIL

DET

VKD

KIAKE

QRa

ttus

norvegicu

sStreptococcus

pyogenes

AID

ELISAminus004minus004minus0053minus004minus004

1

7367

CYGDWA

Hom

osapiens

Triticum

aestivum

OOA

ELISA

7374

CYGLP

DS

EPTK

TNGK

Mus

musculus

Tityus

serrulatus

AID

WIminus002minus0018minus0043minus0023minus0018

7367

CYGDWA

Hom

osapiens

Triticum

aestivum

OOA

ELISA

7374

CYGLP

DS

EPTK

TNGK

Mus

musculus

Tityus

serrulatus

AID

WIminus002minus0018minus0043minus0023minus0018

144610

DFF

TYK

Mus

musculus

Porcine

transmissible

AID

WI

144611

DFN

GSF

DMNGTITA

Oryctolagus

cuniculus

Escherich

iacoli

AID

ELISAminus001minus0007minus0018minus001minus0012

112047

AST

RESG

Hom

osapiens

Hom

osapiens

OAID

ELISA

112048

ATAST

MDHARH

GF

LPRH

RDT

Hom

osapiens

Hom

osapiens

AID

ELISA

005

005

005

0057

005

36136

LGGVFT

Hom

osapiens

Denguev

irus2

OOID

ELISA

36137

LGGWKL

QSD

PRAY

AL

Hom

osapiens

Ambrosia

artemisiifolia

OOA

RIA

001

001

0007

0013

000

9

115256

FREL

KDLK

GY

Hom

osapiens

Bostaurus

DEW

EDWI

115261

GDLE

ILL

QKW

ENG

ECAQ

KKI

Hom

osapiens

Bostaurus

OOA

FIAminus001minus001minus001

0minus000

9

129024

KADQLY

KHom

osapiens

Hom

osapiens

OAID

ELISA

129026

KAKK

PAAAAG

AKK

AKS

Oryctolagus

cuniculus

Hom

osapiens

AID

ELISA

003

0034

003

0037

003

148481

YTRD

LVYK

Rattu

snorvegicu

sHom

osapiens

AID

WI

148483

YVPIVT

FYSE

ISM

HSSRA

IPOryctolagus

cuniculus

MD

AID

ELISA

00002minus0007

0minus0002

150850

GY

Mus

musculus

Hum

anpapillomaviru

sAID

ELISA

150853

HIG

GLSI

LDPIFG

VL

Hom

osapiens

Dermatophagoides

farin

aeOOA

ACAb

B004

0038

004

0043

0039

107366

FPPK

PKD

Hom

osapiens

Hom

osapiens

OAID

ELISA

107376

GDRS

GYS

SPGSP

GMus

musculus

Hom

osapiens

AID

ACAb

Bminus003minus0028minus003minus0023minus0031

114859

ICGTD

GVTY

THom

osapiens

Gallusgallus

OOA

WI

114865

IVER

ETR

GQSE

NPL

WHALR

RRa

ttus

norvegicu

sHum

anherpesvirus

AID

ELISA

004

004

20035

0037

0038

62149

SVHLF

Hom

osapiens

MD

OAID

WI

62150

SVIALG

SQEG

ALH

QALA

GAI

Equu

scaballus

WestN

ilevirus

AID

IFAIHminus002minus0021minus0012minus0013minus0021

98455

SGSV

QFV

PIQ

Mus

musculus

Neisseria

meningitid

isAID

ELISA

98456

SICS

NNP

TCWAICK

RIPN

KKMus

musculus

Hum

anrespira

tory

virus

AID

IFAIH

004

004

0027

004

004

1

61783

STNKA

VVS

LSBo

staurus

Bovine

respira

tory

AID

ELISA

61791

STNPK

PQRK

TKRN

TNRR

PQHom

osapiens

HepatitisC

virus

EWEIR

ACAb

B004

0035

0029

0037

0039

100318

NAPK

TFQ

FIN

Mus

musculus

Bacillusa

nthracis

AID

ELISA

100319

NASSEL

HLL

GFG

INAEN

NHR

Hom

osapiens

Arachish

ypogaea

EWED

ELISA

0minus0002minus0012

00

10 Journal of Immunology ResearchTa

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

118947

PFSA

PPPA

Hom

osapiens

Hom

osapiens

OAID

ELISA

118948

PGAIEQG

PADDPG

EGPS

TGP

Hom

osapiens

Hum

anherpesvirus

NI

ACAb

Bminus004minus004minus004

1minus0036minus004

1

78323

YSFR

DMus

musculus

Bluetongue

virus1

AID

ELISA

78341

AALT

AEN

TAIK

KRN

ADAKA

Hom

osapiens

Streptococcus

mutan

sEE

EEL

ISA

001

0008

0007

0013

001

145831

IPLG

TRP

Mus

musculus

Hum

anpapillomaviru

sAID

ELISA

145841

KEDFR

YAISST

NEI

GLL

GA

Susscrofa

Classicalsw

ine

AID

PACminus004minus004minus0045minus004minus0043

119592

HTF

PAVLQ

Hom

osapiens

Hom

osapiens

OAID

ELISA

119596

IHIPSE

KIWRP

DLV

LYMus

musculus

Hom

osapiens

AID

RIA

001

0012

001

0017

000

9

58780

SKAANLSIIK

MD

Beetnecrotic

MD

WI

58783

SKAFSN

CYPY

DVP

DYA

SLOryctolagus

cuniculus

Influ

enza

Avirus

AID

RIAminus001minus000

4minus0014minus000

9minus0013

115153

MKG

VVC

TRIYEK

VHom

osapiens

Hom

osapiens

NI

ELISA

115155

NNQRK

KAKN

TPFN

MLK

RERN

Mus

musculus

Denguev

irus2

AID

ELISA

001

0012

000

40013

001

133629

LPLR

FOryctolagus

cuniculus

Gallusgallus

AID

ACAb

B133630

LPPG

LHV

FPLA

SNRS

Mus

musculus

MD

AID

SPRminus001minus0013minus0021minus001minus001

96215

EEEE

AE

DKE

DHom

osapiens

Hom

osapiens

OAID

ELISA

96216

EEEG

LLK

KSADTL

WNMQK

Mus

musculus

Mus

musculus

AID

ELISA

008

0082

0078

0087

008

39782

LTAASV

Hom

osapiens

Triticum

aestivum

OOA

ELISA

39788

LTAEL

KIYS

VIQ

AEI

NKH

LOryctolagus

cuniculus

Yersiniapestis

AID

ACAb

B001

0014minus0001

0007

000

9

107479

KFNWYV

DHom

osapiens

Hom

osapiens

OAID

ELISA

107482

KGEP

GL

PGRG

FPGFP

Mus

musculus

Hom

osapiens

AID

ACAb

B0

0002

00007minus0001

63569

TETV

NSD

IMacaca

mulatta

Shigellaflexneri

AID

ELISA

63573

TEVEL

KER

KHRIED

AVRN

AK

Hom

osapiens

Mycobacteriu

mlep

rae

OOID

ELISA

005

0036

004

10049

005

134028

DDTIS

Mus

musculus

Hom

osapiens

AID

NIAA

134029

DED

ENQS

PRSFQKK

TROryctolagus

cuniculus

Hom

osapiens

AID

ELISA

005

0053

005

005

004

8

23028

GVKY

AHom

osapiens

MD

OAID

WI

23032

GVLA

KDV

RFSQ

VHom

osapiens

MD

EWEIR

ACAb

B0

00

000

4minus0003

115293

IMCV

KKILDK

Hom

osapiens

Bostaurus

DEW

EDWI

115295

INPS

KEN

LCST

FCK

EVVRN

AHom

osapiens

Bostaurus

OOA

FIAminus003minus003minus003minus002minus0029

65105

TLTP

ENTL

Mus

musculus

Shigellaflexneri

AID

ELISA

65110

TLTS

GSD

LDRC

TTFD

DV

Oryctolagus

cuniculus

SARS

coronavirus

AID

ELISA

001

0013minus0003

001

001

1344

71PK

PEQ

Mus

musculus

Streptococcus

pneumoniae

AID

FACS

1344

72PL

LPGT

STTS

TGPC

KTHom

osapiens

HepatitisB

virus

AID

ELISA

002

0018

0019

002

0016

Journal of Immunology Research 11Ta

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

142228

LYCY

EQLN

DSSE

Hom

osapiens

Hum

anpapillomaviru

sNI

ELISA

142250

NWGDEP

SKRR

DRS

NSR

GRK

NFelis

catus

Felin

einfectio

usAID

ELISA

007

006

80071

0073

007

21549

GNYD

FWYQ

SHom

osapiens

Staphylococcus

aureus

OOID

ELISA

21553

GNYN

YKY

RYLR

HGK

LRPF

ERMus

musculus

SARS

coronavirus

AID

ELISA

003

0032

0021

0031

003

21549

GNYD

FWYQ

SHom

osapiens

Staphylococcus

aureus

OOID

ELISA

21553

GNYN

YKY

RYLR

HGK

LRPF

EROryctolagus

cuniculus

SARS

coronavirus

AID

ELISA

003

0034

0021

0031

003

34908

LAPL

GE

Hom

osapiens

HepatitisE

virus

EWEIR

ELISA

34914

LAPS

TLRS

LRKR

RLSSP

Hom

osapiens

Hum

anherpesvirus

OOID

ELISA

006

006

0059

0063

006

130454

CLFP

NNSY

CMus

musculus

MD

AID

NIAA

130456

CRPQ

VN

NPK

EWS

CAAC

Hom

osapiens

MD

OOD

ACAb

B001

0008

001

0011

0007

1964

4GFV

PSM

Hom

osapiens

Hepatitisd

elta

virus

OOID

ELISA

1964

7GFV

SASI

FGFQ

AEV

GPN

NTR

Oryctolagus

cuniculus

VacciniavirusW

RAID

ELISAminus001minus000

6minus0013minus000

9minus001

20678

GKRP

EMus

musculus

Streptococcus

pyogenes

AID

WI

20680

GKS

KRD

AKN

NAAK

LAVDKL

LMus

musculus

VacciniavirusW

RAID

WI

001

001

0001

001

001

123278

GYL

KDLP

TTOvisa

ries

Fasciolahepatica

AID

ELISA

123282

HAC

QKK

LLKF

EAL

QQEE

GEE

Rattu

snorvegicu

sGallusgallus

AID

PFFminus002minus0016minus0016minus002minus0025

141067

PLSLEP

DP

Mus

musculus

Hom

osapiens

AID

FACS

141073

REGVRW

RVMAIQ

Mus

musculus

Hom

osapiens

AID

ELISA

006

006

006

006

0056

139248

SFAG

TVIE

Mus

musculus

Classicalsw

ine

AID

WI

139305

TAAQ

ITQ

RKWEA

ARE

AEQ

RROryctolagus

cuniculus

Hom

osapiens

AID

IP003

0033

0026

003

003

104515

HDCR

PKKI

Mus

musculus

LaCrossevirus

AID

IFAIH

104520

IAKE

QE

NKE

TIGT

LKKI

LDE

Rattu

snorvegicu

sStreptococcus

pyogenes

AID

ELISAminus006minus006minus0073minus006minus0061

113517

HLY

ADGLT

DMus

musculus

Hum

anpapillomaviru

sAID

IFAIH

113518

HNKI

QA

IELE

DLL

RYS

KLYR

Mus

musculus

Hom

osapiens

AID

ELISA

002

002

0011

002

0019

156970

VER

HQ

Hom

osapiens

Hom

osapiens

OAID

ELISA

156975

WSSTV

LRVS

PTRT

VP

Mus

musculus

MD

AID

ELISA

001

0012

0003

0017

001

78252

PVQNLT

Mus

musculus

Porphyromonas

gingivalis

AID

WI

78253

QGGCG

RGWAFS

ATG

AIEA

Mus

musculus

Glycinem

axAID

ELISA

002

002

0017

002

0018

6068

CCPD

KNKS

Mus

musculus

Hum

anherpesvirus

AID

WI

6074

CCRH

KQKD

VGDVK

QTL

PPS

Ovisa

ries

MD

AID

ELISA

001

000

6000

4001

0008

12 Journal of Immunology Research

Table3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

53109

RAGVC

YHom

osapiens

Triticum

aestivum

OOA

ELISA

53116

RAILTA

FSPA

QDI

WGTS

Oryctolagus

cuniculus

SARS

coronavirus

AID

ELISAminus002minus0016minus0037minus0023minus002

147041

IPEQ

Hom

osapiens

Triticum

aestivum

OOA

WI

147064

KHQGA

QYV

WN

RTA

Hom

osapiens

Bostaurus

OOID

WI

006

006

0047

0057

006

7066

4VQ

VVYD

YQOryctolagus

cuniculus

Treponem

apallidu

mAID

ELISA

70667

VQWMN

RLIAFA

FAG

NHVS

PHom

osapiens

HepatitisC

virus

OOID

ELISA

005

004

6004

10049

005

134343

DLY

IKMus

musculus

Hum

anpapillomaviru

sAID

PhDIP

134344

DMAQ

VTV

GPG

LLGVS

TLMus

musculus

Hom

osapiens

AID

PhDIP

00minus000

90

0

Journal of Immunology Research 13

The first input term is the value 120582(120576119894119895)ref is the scoring

function 120582 of the efficiency of the initial process 120576119894119895(known

solution) The function 120582(120576119894119895)ref = 1 if the 119894th peptide could

experimentally be demonstrated to be a B-epitope in theassay of reference (reference) carried out in the conditions119888119895 120582(120576119894119895)ref = 0 otherwise The variational-perturbation

terms ΔΔ120594119888119895are at the same time terms typical of pertur-

bation theory and moving average (MA) functions used inBox-Jenkin models in time series [60] These new types ofterms account both for the deviation of the electronegativityof all amino acids in the sequence of the new peptidewith respect to the peptide of reference and with respectto all boundary conditions In Table 1 we give the overallclassification results obtainedwith thismodel Speck-Plancheet al [61ndash63] introduced different multitargetmultiplexingQSARmodels that incorporate this type of information basedon MAs The results obtained with the present model areexcellent comparedwith other similarmodels in the literatureuseful for other problems including moving average models[64 65] or perturbation models [58] Notably this is also thefirst model combining both perturbation theory and MAs ina QSPR context

The other input terms are the following The first Δ120594seq =120594(119898119902)ref minus120594(119898119894)new is the perturbation term for the variation

or in themean value of electronegativity for all amino acids inthe sequence of the peptide of reference The remnant inputvariables of the model ΔΔ120594

119888119895= Δ120594

119888119895-ref minus Δ120594119888119895-new =

[120594(119898119902)ref minus

lowast120594 (119888119902119903)ref] minus [120594(119898119894)new minus

lowast120594 (119888119894119895)new] quantify

values of the conditions of the new assay 119888119895-new that representperturbations with respect to the initial conditions 119888

119894119895-ref of

the assay of reference The quantities lowast120594(119888119894119895) and lowast120594(119888

119902119903) are

the average values of the mean electronegativity values 120594(119898119894)

and 120594(119898119902) for all new and reference peptides in IEDB that

are epitopes under the 119895th or 119903th boundary condition Thevalues of these terms have been tabulated for gt500 sourceorganisms gt50 host organisms gt10 biological process andgt30 experimental techniques We must substitute the valuesof 120594(119898

119894) and 120594(119898

119902) of the new and reference peptides and the

tabulated values of lowast120594(119888119894119895) and lowast120594(119888

119902119903) for all combinations

of boundary conditions to predict the perturbations of theaction as epitope of peptides In doing so we can found theoptimal sequence and boundary conditions towards the useof the peptide in the development of a vaccine In Table 2 wegive some of these values of lowast120594(119888

119894119895) and lowast120594(119888

119902119903)

In Table 3 we depict the sequences and input-outputboundary conditions for top perturbations present in IEDBAll these perturbations have observed value of 120582(120576

119894119895)new = 1

and predicted value also equal to 1 with a high probabilitySee Supplementary Material available online at httpdxdoiorg1011552014768515 file contains a full list of gt200000cases of perturbations

4 Conclusions

It is possible to develop generalmodels for vaccine design ableto predict the results of multiple input-output perturbations

in peptide sequence and experimental assay boundary condi-tions using ideas of QSPR analysis perturbation theory andBox and Jenkins MA operators The electronegativity valuescalculated withMARCH-INSIDE seem to be goodmoleculardescriptors for this type of QSPR-perturbation models

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

The present study was partially supported by GrantsAGL2010-22290-C02 and AGL2011-30563-C03 from Minis-terio de Ciencia e Innovacion Spain and Grant CN 2012155from Xunta de Galicia Spain

References

[1] B Peters J Sidney P Bourne et al ldquoThe design and implemen-tation of the immune epitope database and analysis resourcerdquoImmunogenetics vol 57 no 5 pp 326ndash336 2005

[2] B Peters J Sidney P Bourne et al ldquoThe immune epitopedatabase and analysis resource from vision to blueprintrdquo PLoSBiology vol 3 no 3 p e91 2005

[3] PWang A AMorgan Q Zhang A Sette and B Peters ldquoAuto-mating document classification for the Immune Epitope Data-baserdquo BMC Bioinformatics vol 8 article 269 2007

[4] A Sette ldquoThe immune epitope database and analysis resourcefrom vision to blueprintrdquo Genome Informatics vol 15 no 2 p299 2004

[5] N Salimi W Fleri B Peters and A Sette ldquoThe immune epi-tope database a historical retrospective of the first decaderdquoImmunology vol 137 no 2 pp 117ndash123 2012

[6] Y Kim A Sette and B Peters ldquoApplications for T-cell epitopequeries and tools in the Immune EpitopeDatabase andAnalysisResourcerdquo Journal of Immunological Methods vol 374 no 1-2pp 62ndash69 2011

[7] Y Kim J Ponomarenko Z Zhu D Tamang et al ldquoImmuneepitope database analysis resourcerdquo Nucleic Acids Research ppW525ndashW530 2012

[8] A M Helguera R D Combes M P Gonzalez and M N DS Cordeiro ldquoApplications of 2D descriptors in drug design aDRAGON talerdquo Current Topics in Medicinal Chemistry vol 8no 18 pp 1628ndash1655 2008

[9] GM Casanola-Martın Y Marrero-Ponce M T H Khan et alldquoDragon method for finding novel tyrosinase inhibitors Biosil-ico identification and experimental in vitro assaysrdquo The Euro-pean Journal of Medicinal Chemistry vol 42 no 11-12 pp 1370ndash1381 2007

[10] I V Tetko J Gasteiger R Todeschini et al ldquoVirtual computa-tional chemistry laboratorymdashdesign and descriptionrdquo Journalof Computer-AidedMolecular Design vol 19 no 6 pp 453ndash4632005

[11] A R Katritzky A Oliferenko A Lomaka and M KarelsonldquoSix-membered cyclic ureas as HIV-1 protease inhibitors aQSAR study based on CODESSA PRO approachrdquo Bioorganicand Medicinal Chemistry Letters vol 12 no 23 pp 3453ndash34572002

14 Journal of Immunology Research

[12] A R Katritzky S Perumal R Petrukhin and E Klein-peter ldquoCODESSA-based theoretical QSPRmodel for hydantoinHPLC-RT lipophilicitiesrdquo Journal of Chemical Information andComputer Sciences vol 41 no 3 pp 569ndash574 2001

[13] S Vilar G Cozza and S Moro ldquoMedicinal chemistry and themolecular operating environment (MOE) application of QSARand molecular docking to drug discoveryrdquo Current Topics inMedicinal Chemistry vol 8 no 18 pp 1555ndash1572 2008

[14] G Marzaro A Chilin A Guiotto et al ldquoUsing the TOPS-MODE approach to fit multi-target QSAR models for tyrosinekinases inhibitorsrdquo The European Journal of Medicinal Chem-istry vol 46 no 6 pp 2185ndash2192 2011

[15] S Vilar E Estrada E Uriarte L Santana and Y Gutierrez ldquoInsilico studies toward the discovery of new anti-HIV nucleosidecompounds through the use of tops-mode and 2D3D con-nectivity indices 2 Purine derivativesrdquo Journal of ChemicalInformation and Modeling vol 45 no 2 pp 502ndash514 2005

[16] E Estrada J A Quincoces and G Patlewicz ldquoCreating molec-ular diversity from antioxidants in Brazilian propolis combina-tion of TOPS-MODE QSAR and virtual structure generationrdquoMolecular Diversity vol 8 no 1 pp 21ndash33 2004

[17] E Estrada andHGonzalez ldquoWhat are the limits of applicabilityfor graph theoretic descriptors in QSPRQSAR modelingdipole moments of aromatic compounds with TOPS-MODEdescriptorsrdquo Journal of Chemical Information and ComputerSciences vol 43 no 1 pp 75ndash84 2003

[18] Y Marrero-Ponce J A Castillo-Garit E Olazabal et al ldquoTom-ocomd-Cardd a novel approach for computer-aided ldquorationalrdquodrug design I Theoretical and experimental assessment of apromising method for computational screening and in silicodesign of new anthelmintic compoundsrdquo Journal of Computer-Aided Molecular Design vol 18 no 10 pp 615ndash634 2004

[19] Y Marrero Ponce R Medina Marrero E A Castro et al ldquoPro-tein quadratic indices of the ldquomacromolecular pseudographrsquos 120572-carbon atom adjacency matrixrdquo 1 prediction of arc repressoralanine-mutantrsquos stabilityrdquoMolecules vol 9 no 12 pp 1124ndash11472004

[20] H Gonzalez-Dıaz F Prado-Prado and F M Ubeira ldquoPredict-ing antimicrobial drugs and targets with the MARCH-INSIDEapproachrdquo Current Topics in Medicinal Chemistry vol 8 no 18pp 1676ndash1690 2008

[21] R Garcıa-Domenech J Galvez J V de Julian-Ortiz and LPogliani ldquoSomenew trends in chemical graph theoryrdquoChemicalReviews vol 108 no 3 pp 1127ndash1169 2008

[22] E Estrada and E Uriarte ldquoRecent advances on the role of topo-logical indices in drug discovery researchrdquo Current MedicinalChemistry vol 8 no 13 pp 1573ndash1588 2001

[23] R Todeschini andVConsonniHandbook ofMolecularDescrip-tors Wiley-VCH Weinheim Germany 2008

[24] E Estrada E J Delgado J B Alderete and G A Jana ldquoQuan-tum-connectivity descriptors inmodeling solubility of environ-mentally important organic compoundsrdquo Journal of Computa-tional Chemistry vol 25 no 14 pp 1787ndash1796 2004

[25] E Besalu X Girones L Amat andR Carbo-Dorca ldquoMolecularquantum similarity and the fundamentals of QSARrdquo Accountsof Chemical Research vol 35 no 5 pp 289ndash295 2002

[26] D A Rincon M N D S Cordeiro and R A Mosquera ldquoOnthe electronic structure of cocaine and its metabolitesrdquo Journalof Physical Chemistry A vol 113 no 50 pp 13937ndash13942 2009

[27] M Mandado M J Gonzalez-Moa and R A MosqueraldquoChemical graph theory and n-center electron delocalization

indices a study on polycyclic aromatic hydrocarbonsrdquo Journalof Computational Chemistry vol 28 no 10 pp 1625ndash1633 2007

[28] THill andP Lewicki STATISTICSMethods andApplications AComprehensive Reference for Science Industry and DataMiningStatSoft Tulsa Okla USA 2006

[29] E Frank M Hall L Trigg G Holmes and I H Witten ldquoDatamining in bioinformatics using Wekardquo Bioinformatics vol 20no 15 pp 2479ndash2481 2004

[30] G Patlewicz N Ball E D Booth E Hulzebos E Zvinavasheand C Hennes ldquoUse of category approaches read-across and(Q)SAR general considerationsrdquo Regulatory Toxicology andPharmacology vol 67 no 1 pp 1ndash12 2013

[31] D W Roberts and G Y Patlewicz ldquoUpdating the skin sensiti-zation in vitro data assessment paradigm in 2009mdasha chemistryand QSAR perspectiverdquo Journal of Applied Toxicology vol 30no 3 pp 286ndash288 2010

[32] D W Roberts and G Y Patlewicz ldquoNonanimal alternatives forskin sensitization letter to the editorrdquoToxicological Sciences vol106 no 2 pp 572ndash574 2008

[33] E Estrada G Patlewicz and Y Gutierrez ldquoFrom knowledgegeneration to knowledge archive a general strategy usingTOPS-MODE with DEREK to formulate new alerts for skinsensitizationrdquo Journal of Chemical Information and ComputerSciences vol 44 no 2 pp 688ndash698 2004

[34] G F Gerberick C A Ryan P S Kern et al ldquoA chemical datasetfor evaluation of alternative approaches to skin-sensitizationtestingrdquo Contact Dermatitis vol 50 no 5 pp 274ndash288 2004

[35] G Y Patlewicz D A Basketter C K Smith Pease et al ldquoFurtherevaluation of quantitative structurendashactivity relationship mod-els for the prediction of the skin sensitization potency ofselected fragrance allergensrdquo Contact Dermatitis vol 50 no 2pp 91ndash97 2004

[36] E Estrada G Patlewicz M Chamberlain D Basketter andS Larbey ldquoComputer-aided knowledge generation for under-standing skin sensitization mechanisms the TOPS-MODEapproachrdquo Chemical Research in Toxicology vol 16 no 10 pp1226ndash1235 2003

[37] G Patlewicz R Rodford and J D Walker ldquoQuantitative struc-ture-activity relationships for predicting skin and eye irritationrdquoEnvironmental Toxicology and Chemistry vol 22 no 8 pp1862ndash1869 2003

[38] G Y Patlewicz R A Rodford G Ellis and M D Barratt ldquoAQSAR model for the eye irritation of cationic surfactantsrdquo Tox-icology In Vitro vol 14 no 1 pp 79ndash84 2000

[39] E Tenorio-Borroto C G Penuelas Rivas J C VasquezChagoyan N Castanedo F J Prado-Prado and X Garcia-Mera ldquoANN multiplexing model of drugs effect on macro-phages theoretical and flow cytometry study on the cyto-toxicity of the anti-microbial drug G1 in spleenrdquo Bioorganic ampMedicinal Chemistry vol 20 no 20 pp 6181ndash6194 2012

[40] D R Flower H McSparron M J Blythe et al ldquoComputationalvaccinology quantitative approachesrdquo Novartis FoundationSymposium vol 254 pp 102ndash120 2003

[41] I A Doytchinova P Guan and D R Flower ldquoQuantitativestructure-activity relationships and the prediction of MHCsupermotifsrdquoMethods vol 34 no 4 pp 444ndash453 2004

[42] Y Xiao and M R Segal ldquoPrediction of genomewide conservedepitope profiles of HIV-1 classifier choice and peptide rep-resentationrdquo Statistical Applications in Genetics and MolecularBiology vol 4 no 1 article 25 2005

Journal of Immunology Research 15

[43] T Fagerberg V Zoete S Viatte P Baumgaertner P M Alves PRomero et al ldquoPrediction of cross-recognition of peptide-HLAA2 by melan-a-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationshipsrdquo PLoSONE vol 8 no 7 Article ID e65590 2013

[44] D Barh A N Misra A Kumar and A Vasco ldquoA novel strategyof epitope design inNeisseria gonorrhoeaerdquo Bioinformation vol5 no 2 pp 77ndash82 2010

[45] J Bi R Song H Yang et al ldquoStepwise identification of HLA-Alowast0201-restricted CD8+ T-cell epitope peptides from herpessimplex virus type 1 genome boosted by a steprank schemerdquoBiopolymers vol 96 no 3 pp 328ndash339 2011

[46] R D Bremel and E J Homan ldquoAn integrated approach to epit-ope analysis II a system for proteomic-scale prediction ofimmunological characteristicsrdquo Immunome Research vol 6 no1 article 8 2010

[47] C M Diez-Rivero B Chenlo P Zuluaga and P A RecheldquoQuantitative modeling of peptide binding to TAP using sup-port vector machinerdquo Proteins vol 78 no 1 pp 63ndash72 2010

[48] E Martınez-Naves E M Lafuente and P A Reche ldquoRecogni-tion of the ligand-type specificity of classical and non-classicalMHC I proteinsrdquo FEBS Letters vol 585 no 21 pp 3478ndash34842011

[49] M Bhasin E L Reinherz and P A Reche ldquoRecognition andclassification of histones using support vectormachinerdquo Journalof Computational Biology vol 13 no 1 pp 102ndash112 2006

[50] P A Reche and E L Reinherz ldquoPEPVAC a web server formulti-epitope vaccine development based on the prediction ofsupertypic MHC ligandsrdquo Nucleic Acids Research vol 33 no 2pp W138ndashW142 2005

[51] E M Lafuente and P A Reche ldquoPrediction of MHC-peptidebinding a systematic and comprehensive overviewrdquo CurrentPharmaceutical Design vol 15 no 28 pp 3209ndash3220 2009

[52] A Gaulton L J Bellis A P Bento et al et al ldquoA large-scalebioactivity database for drug discoveryrdquoNucleic Acids Researchpp D1100ndashD1107 2012

[53] W H Cropper Great Physicists The Life and Times of LeadingPhysicists from Galileo to Hawking Oxford University PressNew York NY USA 2004

[54] E L Bouzarth A Brooks R Camassa et al ldquoEpicyclic orbits ina viscous fluid about a precessing rod theory and experimentsat the micro-and macro-scalesrdquo Physical Review E vol 76 no1 part 2 Article ID 016313 2007

[55] M Laberge ldquoIntrinsic protein electric fields basic non-covalentinteractions and relationship to protein-induced Stark effectsrdquoBiochimica et BiophysicaActa vol 1386 no 2 pp 305ndash330 1998

[56] FM Fernandez and J AMorales ldquoPerturbation theorywithoutwave functions for the Zeeman effect in hydrogenrdquo PhysicalReview A vol 46 no 1 pp 318ndash326 1992

[57] B D Marshall and W G Chapman ldquoThermodynamic pertur-bation theory for associating fluids with small bond angleseffects of steric hindrance ring formation and double bondingrdquoPhysical Reiew E vol 87 no 5 Article ID 052307 12 pages 2013

[58] H Gonzalez-Diaz S Arrasate A Gomez-San Juan et al ldquoNewtheory for multiple input-output perturbations in complexmolecular systems 1 Linear QSPR electronegativity models inphysical organic and medicinal chemistryrdquo Current Topics inMedicinal Chemistry 2013

[59] H Gonzalez-Diaz S Arrasate N Sotomayor et al ldquoMIANNmodels in medicinal physical and organic chemistryrdquo CurrentTopics in Medicinal Chemistry vol 13 no 5 pp 619ndash641 2013

[60] G E P Box and GM Jenkins Time Series Analysis Forecastingand Control Holden-Day San Francisco Calif USA 1970

[61] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoUnified multi-target approach for the rational insilico design of anti-bladder cancer agentsrdquo Anti-Cancer Agentsin Medicial Chemistry vol 13 no 5 pp 791ndash800 2013

[62] A Speck-Planche V V Kleandrova and M N Cordeiro ldquoNewinsights toward the discovery of antibacterial agents multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugsrdquo TheEuropean Journal of Pharmaceutical Sciences vol 48 no 4-5pp 812ndash818 2013

[63] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoMulti-target inhibitors for proteins associated withAlzheimer in silico discovery using fragment-based descrip-torsrdquo Current Alzheimer Research vol 10 no 2 pp 117ndash1242013

[64] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoIn silico discovery and virtual screening of multi-target inhibitors for proteins in Mycobacterium tuberculosisrdquoCombinatorial Chemistry amp High Throughput Screening vol 15no 8 pp 666ndash673 2012

[65] A Speck-Planche V V Kleandrova F Luan and M N DS Cordeiro ldquoChemoinformatics in anti-cancer chemotherapymulti-target QSAR model for the in silico discovery of anti-breast cancer agentsrdquo The European Journal of PharmaceuticalSciences vol 47 no 1 pp 273ndash279 2012

Submit your manuscripts athttpwwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MEDIATORSINFLAMMATION

of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Behavioural Neurology

EndocrinologyInternational Journal of

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Disease Markers

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BioMed Research International

OncologyJournal of

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Oxidative Medicine and Cellular Longevity

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PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of

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Computational and Mathematical Methods in Medicine

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Diabetes ResearchJournal of

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Research and TreatmentAIDS

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Gastroenterology Research and Practice

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Page 5: Research Article Model for Vaccine Design by …downloads.hindawi.com/journals/jir/2014/768515.pdfResearch Article Model for Vaccine Design by Prediction of B-Epitopes of IEDB Given

Journal of Immunology Research 5

Table 2 Continued

Source organism (so) 119873in 119873outlowast120594

Canis lupus 6604 3334 26906Macaca mulatta 5261 2569 26993Ovis aries 3953 1653 26836Equus caballus 3943 2099 26842Cavia porcellus 3458 1688 26833Capra hircus 2182 1127 26830Aotus nancymaae 1659 852 26837Pan troglodytes 1614 732 26757Marmota monax 1100 509 27011Felis catus 901 279 26838Myodes glareolus 814 388 26863Anas platyrhynchos 688 342 26880Homo sapiens (human) 508 270 26851Trichosurus vulpecula 456 126 26921Mesocricetus auratus 438 104 26909Macaca cyclopis 382 193 26871O tshawytscha 333 159 26929Macaca fuscata 188 100 26667Cricetulus migratorius 171 142 27008Camelus dromedarius 171 89 26886Dicentrarchus labrax 121 55 26759Macaca fascicularis 96 52 26793Saimiri sciureus 92 44 26900Canis familiaris 77 42 26850Rattus rattus 72 31 26760Callithrix pygmaea 67 30 26920Chinchilla lanigera 41 24 26729Aotus lemurinus 30 19 26860Papio cynocephalus 27 13 27267Aotus griseimembra 26 12 27000Mustela vison 18 10 27000Chlorocebus aethiops 15 10 26875Bos indicus 13 4 26925Oncorhynchus mykiss 10 4 26700M macquariensis 9 6 26600Cricetulus griseus 8 4 26900Aotus trivirgatus 7 4 27000Process type (pt) 119873in 119873out

lowast120594

AID 111197 108536 26876OOID 32419 32617 26868OAID 19210 18954 26801OOA 15863 16303 26902NI 13430 15206 26845EWEIR 4818 4864 26843EEE 3113 3546 26906OOD 2806 2799 26887AICD 1077 1095 26812EWED 696 686 26879DEWED 280 337 26804TT 260 215 26806OOC 153 137 26800

Table 2 Continued

Source organism (so) 119873in 119873outlowast120594

Technique (tq) 119873in 119873outlowast120594

ELISA 133458 135109 26871WI 33627 33292 26887ACAbB 7780 9068 26862PhDIP 7450 4496 26771RIA 5241 5218 26858IFAIH 4454 4581 26879NIAA 4222 4316 26892FIA 2255 2276 26897PAC 1312 1219 26837IP 1127 1089 26886SPR 758 639 26860FACS 608 647 26907Other 502 495 26813SAC 484 393 26878ELISPOT 396 412 26979RDAT 366 323 26859EDAT 284 330 26800XRC 231 227 26880MS 209 179 26849PFF 171 153 26820AbDPO 162 295 26968CdC 146 205 26895IAbBA 144 183 26940IOT 124 106 26835HAGGI 115 122 26834IgMHR 89 90 26929EAAA 84 139 26922HS 82 67 26791AbdCC 73 118 26897AGG 50 60 26980CM 50 57 26863The lowastindicates that quantities like lowast120594 is the average value of the meanelectronegativity (119898119894) for all the peptides in IEDB that are epitopes for thesame boundary condition

3 Results and Discussion

We propose herein for the first time a QSRR-perturbationmodel able to predict variations in the propensity of a peptideto act as B-epitope taking into consideration the propensityof a peptide of reference and the changes in peptide sequenceimmunological process host organism source organismsand the experimental technique used The best QSPR-perturbation model found here with LDA was

120582(120576119894119895)new

= 4979 sdot 120582(120576119894119895)refminus 221642 sdot Δ120594seq

+ 8770 sdot ΔΔ120594ho + 63572 sdot ΔΔ120594so

minus 55387 sdot ΔΔ120594ip + 201919 sdot ΔΔ120594tq minus 2149

119873 = 155169 119877119888 = 092

119880 = 015 119901 lt 001

(6)

6 Journal of Immunology Research

Table3To

p100

values

ofp1

forp

ositive

perturbatio

nsin

training

serie

sNew

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

115153

MKG

VVC

TRIYEK

VHom

osapiens

Hom

osapiens

OAID

ELISA

115155

NNQRK

KAKN

TPFN

MLK

RERN

Mus

musculus

Denguev

irus2

AID

WI

001

0012

000

40017

0012

52124

QQQPP

Hom

osapiens

Triticum

aestivum

OOA

ELISA

52128

QQQQGG

SQSQ

KGKQ

QHom

osapiens

Glycinem

axOOA

WI

00minus0018

00002

3639

APL

GVT

Hom

osapiens

HepatitisE

virus

EWEIR

ELISA

3652

APL

TRG

SCRK

RNRS

PER

Hom

osapiens

Hum

anherpesvirus

OOID

ELISA

004

004

0039

0043

004

135959

LTRA

YAKD

VKF

GHom

osapiens

Hom

osapiens

OAID

ELISA

135963

NGQEE

KAG

VVS

TGLIGGG

Mus

musculus

MD

AID

ELISAminus004minus0038minus0047minus0033minus004

108075

PREP

QVY

Hom

osapiens

Hom

osapiens

OAID

ELISA

108078

PTSP

SGV

EEWIV

TQVVPG

VAOryctolagus

cuniculus

Hom

osapiens

AID

ACAb

Bminus001minus000

6minus001minus0003minus0011

25113

HVVDLP

Hom

osapiens

HepatitisE

virus

EWEIR

ELISA

25126

HWGNH

SKSH

PQR

Mus

musculus

MD

AID

ELISA

002

0022

0013

0023

002

48780

PPFSPQ

Hom

osapiens

Hepatitisd

elta

virus

OOID

ELISA

48782

PPFT

SAV

GGVDHRS

Mus

musculus

MD

AID

SAC

002

0022

0008

0021

0021

40988

LYVVA

YQA

Mus

musculus

Viscum

album

AID

ELISA

4100

4MAARL

CCQLD

PARD

VHom

osapiens

HepatitisB

virus

OOID

ELISA

002

0018

000

60019

002

50439

QDAY

NAAG

Mus

musculus

Mycobacteriu

mscrofulaceum

AID

ELISA

5044

5QDCN

CSI

YPGHAS

GHRM

AWD

Hom

osapiens

HepatitisC

virus

OOID

ELISA

004

0038

0028

0039

004

98849

KIPA

VFK

IDA

Hom

osapiens

Bostaurus

DEW

EDWI

98850

KKGSE

EEGDITNPIN

Hom

osapiens

Arachish

ypogaea

OOA

IFAIHminus005minus005minus0053minus004minus0051

116171

TQDQDP

BBHFF

KNIV

TPR

Hom

osapiens

Hom

osapiens

OAID

ACAb

B116

286

CGKG

LSAT

VTG

GQKG

RGSR

Oryctolagus

cuniculus

Mus

musculus

AID

MS

001

0014

0008

0017

000

9

123442

LLKD

LRKN

Hom

osapiens

Bornadiseasev

irus

EWEIR

WI

123443

LLTE

HR

MTW

DPA

QPP

RDLT

EHom

osapiens

Hom

osapiens

OOID

ELISAminus002minus002minus0025minus0017minus0022

47858

PHVVDL

Hom

osapiens

HepatitisE

virus

EWEIR

ELISA

47860

PHWIK

KPNRQ

GLG

YYS

Caprahircus

Hum

anT-lymphotropic

virus

AID

ACAb

B001

0007

0005

0013

000

9

61783

STNKA

VVS

LSBo

staurus

Bovine

respira

tory

AID

ELISA

61792

STNPK

PQRK

TKRN

TNRR

PQD

Hom

osapiens

HepatitisC

virus

OOID

ELISA

003

0025

0019

0029

003

118210

VMLY

QISEE

Hom

osapiens

Hom

osapiens

OAID

WI

118217

VTK

YITK

GWKE

VH

Oryctolagus

cuniculus

Hom

osapiens

AID

ELISA

005

0054

005

0057

004

8

130944

LFKH

SOryctolagus

cuniculus

Rattu

snorvegicus

AID

ELISA

130956

LPPR

VTP

KWSLDA

WST

WR

Hom

osapiens

MD

OOD

WI

001

000

6minus0002

0011

0012

Journal of Immunology Research 7Ta

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

23028

GVKY

AHom

osapiens

MD

OAID

WI

23032

GVLA

KDVRF

SQV

Hom

osapiens

MD

OOID

ELISA

00

00007minus0002

51199

QKK

AIE

Oryctolagus

cuniculus

Vibriocholerae

AID

ELISA

51204

QKK

NK

RNTN

RRPQ

DV

Hom

osapiens

HepatitisC

virus

OOID

ELISA

003

0026

0018

0029

003

144783

SHVVT

MLD

NF

Hom

osapiens

Hom

osapiens

NI

ELISA

144786

SMNRG

RGTH

PSLIWM

Mus

musculus

MD

AID

ACAb

B003

0032

0023

0033

0029

134343

DLY

IKMus

musculus

Hum

anpapillomaviru

sAID

NIAA

134344

DMAQ

VTV

GPG

LLGVS

TLMus

musculus

Hom

osapiens

AID

WI

00minus000

90

0

38321

LNQLA

GRM

Anas

platyrhynchos

Duckhepatitis

AID

ELISA

38323

LNQTA

RAFP

DCA

ICW

EPSP

POryctolagus

cuniculus

Bovine

leukemia

virus

AID

ACAb

Bminus001minus0008minus0022minus001minus0011

144657

GQITVD

MMYG

Hom

osapiens

Hom

osapiens

OAID

ELISA

1446

61GRE

GYP

ADGGCA

WPA

CYC

Oryctolagus

cuniculus

MD

AID

WI

002

0024

0013

0027

0022

21084

GLQ

NMus

musculus

Chlamydia

trachom

atis

AID

ELISA

21093

GLR

AQD

DFS

GWDI

NTP

AFE

WMus

musculus

Mycobacteriu

mtuberculosis

AID

WI

003

003

0013

003

0032

98453

SGFS

GSV

QFV

Oryctolagus

cuniculus

Neisseria

meningitid

isAID

ELISA

98456

SICS

NN

PTCW

AIC

KRIPNKK

Mus

musculus

Hum

anrespira

tory

virus

AID

IFAIH

004

0037

0027

004

004

1

98453

SGFS

GSV

QFV

Mus

musculus

Neisseria

meningitid

isAID

ELISA

98456

SICS

NN

PTCW

AIC

KRIPNKK

Mus

musculus

Hum

anrespira

tory

virus

AID

IFAIH

004

004

0027

004

004

1

107107

EAIQ

PRa

ttus

norvegicu

sHom

osapiens

AID

ELISA

107110

EKER

RPSP

IGTA

TLL

Hom

osapiens

MD

OOA

ELISA

005

004

80043

0053

005

110857

FTGEA

YSY

WSA

KHom

osapiens

Mycoplasm

apenetra

nsEW

EIR

ELISA

110859

GEE

SRIS

LPLP

NF

SSLN

LRE

Mus

musculus

Hom

osapiens

AID

FIA

00002minus0017

0003

0003

36315

LGSA

YPMus

musculus

Mycobacteriu

mlep

rae

MD

ELISA

36317

LGSG

AFG

TIYK

GMus

musculus

Avian

erythroblasto

sisvirus

AID

ACAb

B001

001

00011

000

9

122034

WNPA

DRa

ttus

norvegicu

sTorpedocalifornica

AID

ELISA

122035

WNPA

DYG

GIK

WN

PADYG

GIK

Rattu

snorvegicu

sMD

AID

RIA

001

001

0001

001

000

9

25013

HVA

DID

KLID

Mus

musculus

Puum

alavirus

Kazan

AID

ELISA

25021

HVA

PTH

YVTE

SDA

SQRV

TQL

Hom

osapiens

HepatitisC

virus

OOID

ELISA

0minus0002minus0014minus0001

0

36162

LGIH

EOryctolagus

cuniculus

Cand

idaalbicans

AID

ELISA

36166

LGIM

GE

YRGTP

RNQDLY

DAA

Mus

musculus

Hum

anrespira

tory

virus

AID

RIA

0minus0003minus0007

0minus0001

67253

TWEV

LHMus

musculus

Plasmodium

vivax

AID

ELISA

67257

TWGEN

ETDVLL

LNNTR

PPQ

Hom

osapiens

HepatitisC

virus

OOID

ACAb

Bminus002minus0022minus0027minus0021minus0021

8 Journal of Immunology Research

Table3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

50990

QGYR

VSSY

LPHom

osapiens

Hevea

brasiliensis

OOA

WI

50998

QHEQ

DR

PTPS

PAP

SRPF

SVL

Hom

osapiens

HepatitisE

virus

OOID

ELISA

001

001minus0002

0007

0008

100458

RDVLQ

LYAPE

Mus

musculus

Bacillusa

nthracis

AID

ELISA

1004

62RF

STRY

GNQNGRI

RVLQ

RFD

Hom

osapiens

Arachish

ypogaea

EWED

ELISA

003

0028

0018

003

003

111036

TEST

FTGEA

YSV

Hom

osapiens

Mycoplasm

apenetra

nsEW

EIR

ELISA

111039

TGVPID

PAVPD

SSIV

PLLE

SBo

staurus

Bovine

papillomaviru

sAID

ELISA

003

0035

002

0033

003

117919

IFIEME

Hom

osapiens

Hom

osapiens

OAID

WI

117921

IGIIDLIE

KRKF

NQ

Mus

musculus

Hom

osapiens

AID

WI

003

0032

003

0037

003

7127

CTDTD

KLF

Oryctolagus

cuniculus

Shigellaflexneri

AID

ELISA

7128

CTDVS

TAIH

ADQL

TPAW

Hom

osapiens

SARS

coronavirus

OOID

ELISA

001

000

6minus0003

000

9001

112253

PGQSP

KLHom

osapiens

Hom

osapiens

OAID

ELISA

112255

PIRA

LVGDEV

ELP

CRISPG

KMus

musculus

Hom

osapiens

AID

ELISA

001

0012

001

0017

001

122034

WNPA

DRa

ttus

norvegicu

sTorpedocalifornica

AID

ELISA

122038

WNPD

DY

GGVKW

NP

DDYG

GVK

Rattu

snorvegicu

sMD

AID

RIA

00minus000

90minus0001

131878

FLMLV

GGST

LHom

osapiens

Hom

osapiens

OAID

ACAb

B131879

FLVA

HT

RARA

PSA

GER

ARR

SMus

musculus

Mus

musculus

AID

NIAA

003

0032

0028

0037

0033

7066

4VQ

VVYD

YQHom

osapiens

Treponem

apallidu

mOOID

ELISA

70667

VQWMNR

LIAFA

FAG

NHVS

PHom

osapiens

HepatitisC

virus

OOID

ELISA

005

005

004

1005

005

71545

VTV

Hom

osapiens

Helicobacterpylori

OOID

ELISA

71559

VTV

RGGL

RILSPD

RKHom

osapiens

Arachish

ypogaea

OOA

WI

004

004

0032

0043

004

2

127856

TDVRY

KD

Mus

musculus

Mus

musculus

AID

ACAb

B127857

TDVRY

KDDMYH

FFCP

AIQ

AQMus

musculus

Mus

musculus

AID

PFF

001

001

001

001

000

6

112149

GVG

WIRQ

Hom

osapiens

Hom

osapiens

OAID

ELISA

112152

HHPA

RTAHYG

SLP

QKS

HGRT

Hom

osapiens

Hom

osapiens

AID

ELISA

00

00007

0

119581

FSCS

VMHE

Hom

osapiens

Hom

osapiens

OAID

ELISA

119582

GLQ

LIQL

INVDEV

NQI

Mus

musculus

Hom

osapiens

AID

RIAminus001minus0008minus001minus0003minus0011

144657

GQITVD

MMYG

Hom

osapiens

Hom

osapiens

OAID

ELISA

144659

GRE

GYP

ADGGAA

GYC

NTE

Oryctolagus

cuniculus

MD

AID

WIminus001minus000

6minus0017minus0003minus0008

25013

HVA

DI

DKL

IDMus

musculus

Puum

alavirus

Kazan

AID

ELISA

25022

HVA

PTH

YVVES

DA

SQRV

TQV

Hom

osapiens

HepatitisC

virus

OOID

ELISA

0minus0002minus0014minus0001

0

Journal of Immunology Research 9Ta

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

144652

GMRG

MKG

LVY

Hom

osapiens

Hom

osapiens

OAID

ELISA

144654

GPH

PTLE

VVPM

GRG

SMus

musculus

MD

AID

ELISAminus002minus0018minus0027minus0013minus002

104515

HDCR

PKKI

Mus

musculus

LaCrossevirus

AID

IFAIH

104521

IGTL

KKIL

DET

VKD

KIAKE

QRa

ttus

norvegicu

sStreptococcus

pyogenes

AID

ELISAminus004minus004minus0053minus004minus004

1

7367

CYGDWA

Hom

osapiens

Triticum

aestivum

OOA

ELISA

7374

CYGLP

DS

EPTK

TNGK

Mus

musculus

Tityus

serrulatus

AID

WIminus002minus0018minus0043minus0023minus0018

7367

CYGDWA

Hom

osapiens

Triticum

aestivum

OOA

ELISA

7374

CYGLP

DS

EPTK

TNGK

Mus

musculus

Tityus

serrulatus

AID

WIminus002minus0018minus0043minus0023minus0018

144610

DFF

TYK

Mus

musculus

Porcine

transmissible

AID

WI

144611

DFN

GSF

DMNGTITA

Oryctolagus

cuniculus

Escherich

iacoli

AID

ELISAminus001minus0007minus0018minus001minus0012

112047

AST

RESG

Hom

osapiens

Hom

osapiens

OAID

ELISA

112048

ATAST

MDHARH

GF

LPRH

RDT

Hom

osapiens

Hom

osapiens

AID

ELISA

005

005

005

0057

005

36136

LGGVFT

Hom

osapiens

Denguev

irus2

OOID

ELISA

36137

LGGWKL

QSD

PRAY

AL

Hom

osapiens

Ambrosia

artemisiifolia

OOA

RIA

001

001

0007

0013

000

9

115256

FREL

KDLK

GY

Hom

osapiens

Bostaurus

DEW

EDWI

115261

GDLE

ILL

QKW

ENG

ECAQ

KKI

Hom

osapiens

Bostaurus

OOA

FIAminus001minus001minus001

0minus000

9

129024

KADQLY

KHom

osapiens

Hom

osapiens

OAID

ELISA

129026

KAKK

PAAAAG

AKK

AKS

Oryctolagus

cuniculus

Hom

osapiens

AID

ELISA

003

0034

003

0037

003

148481

YTRD

LVYK

Rattu

snorvegicu

sHom

osapiens

AID

WI

148483

YVPIVT

FYSE

ISM

HSSRA

IPOryctolagus

cuniculus

MD

AID

ELISA

00002minus0007

0minus0002

150850

GY

Mus

musculus

Hum

anpapillomaviru

sAID

ELISA

150853

HIG

GLSI

LDPIFG

VL

Hom

osapiens

Dermatophagoides

farin

aeOOA

ACAb

B004

0038

004

0043

0039

107366

FPPK

PKD

Hom

osapiens

Hom

osapiens

OAID

ELISA

107376

GDRS

GYS

SPGSP

GMus

musculus

Hom

osapiens

AID

ACAb

Bminus003minus0028minus003minus0023minus0031

114859

ICGTD

GVTY

THom

osapiens

Gallusgallus

OOA

WI

114865

IVER

ETR

GQSE

NPL

WHALR

RRa

ttus

norvegicu

sHum

anherpesvirus

AID

ELISA

004

004

20035

0037

0038

62149

SVHLF

Hom

osapiens

MD

OAID

WI

62150

SVIALG

SQEG

ALH

QALA

GAI

Equu

scaballus

WestN

ilevirus

AID

IFAIHminus002minus0021minus0012minus0013minus0021

98455

SGSV

QFV

PIQ

Mus

musculus

Neisseria

meningitid

isAID

ELISA

98456

SICS

NNP

TCWAICK

RIPN

KKMus

musculus

Hum

anrespira

tory

virus

AID

IFAIH

004

004

0027

004

004

1

61783

STNKA

VVS

LSBo

staurus

Bovine

respira

tory

AID

ELISA

61791

STNPK

PQRK

TKRN

TNRR

PQHom

osapiens

HepatitisC

virus

EWEIR

ACAb

B004

0035

0029

0037

0039

100318

NAPK

TFQ

FIN

Mus

musculus

Bacillusa

nthracis

AID

ELISA

100319

NASSEL

HLL

GFG

INAEN

NHR

Hom

osapiens

Arachish

ypogaea

EWED

ELISA

0minus0002minus0012

00

10 Journal of Immunology ResearchTa

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

118947

PFSA

PPPA

Hom

osapiens

Hom

osapiens

OAID

ELISA

118948

PGAIEQG

PADDPG

EGPS

TGP

Hom

osapiens

Hum

anherpesvirus

NI

ACAb

Bminus004minus004minus004

1minus0036minus004

1

78323

YSFR

DMus

musculus

Bluetongue

virus1

AID

ELISA

78341

AALT

AEN

TAIK

KRN

ADAKA

Hom

osapiens

Streptococcus

mutan

sEE

EEL

ISA

001

0008

0007

0013

001

145831

IPLG

TRP

Mus

musculus

Hum

anpapillomaviru

sAID

ELISA

145841

KEDFR

YAISST

NEI

GLL

GA

Susscrofa

Classicalsw

ine

AID

PACminus004minus004minus0045minus004minus0043

119592

HTF

PAVLQ

Hom

osapiens

Hom

osapiens

OAID

ELISA

119596

IHIPSE

KIWRP

DLV

LYMus

musculus

Hom

osapiens

AID

RIA

001

0012

001

0017

000

9

58780

SKAANLSIIK

MD

Beetnecrotic

MD

WI

58783

SKAFSN

CYPY

DVP

DYA

SLOryctolagus

cuniculus

Influ

enza

Avirus

AID

RIAminus001minus000

4minus0014minus000

9minus0013

115153

MKG

VVC

TRIYEK

VHom

osapiens

Hom

osapiens

NI

ELISA

115155

NNQRK

KAKN

TPFN

MLK

RERN

Mus

musculus

Denguev

irus2

AID

ELISA

001

0012

000

40013

001

133629

LPLR

FOryctolagus

cuniculus

Gallusgallus

AID

ACAb

B133630

LPPG

LHV

FPLA

SNRS

Mus

musculus

MD

AID

SPRminus001minus0013minus0021minus001minus001

96215

EEEE

AE

DKE

DHom

osapiens

Hom

osapiens

OAID

ELISA

96216

EEEG

LLK

KSADTL

WNMQK

Mus

musculus

Mus

musculus

AID

ELISA

008

0082

0078

0087

008

39782

LTAASV

Hom

osapiens

Triticum

aestivum

OOA

ELISA

39788

LTAEL

KIYS

VIQ

AEI

NKH

LOryctolagus

cuniculus

Yersiniapestis

AID

ACAb

B001

0014minus0001

0007

000

9

107479

KFNWYV

DHom

osapiens

Hom

osapiens

OAID

ELISA

107482

KGEP

GL

PGRG

FPGFP

Mus

musculus

Hom

osapiens

AID

ACAb

B0

0002

00007minus0001

63569

TETV

NSD

IMacaca

mulatta

Shigellaflexneri

AID

ELISA

63573

TEVEL

KER

KHRIED

AVRN

AK

Hom

osapiens

Mycobacteriu

mlep

rae

OOID

ELISA

005

0036

004

10049

005

134028

DDTIS

Mus

musculus

Hom

osapiens

AID

NIAA

134029

DED

ENQS

PRSFQKK

TROryctolagus

cuniculus

Hom

osapiens

AID

ELISA

005

0053

005

005

004

8

23028

GVKY

AHom

osapiens

MD

OAID

WI

23032

GVLA

KDV

RFSQ

VHom

osapiens

MD

EWEIR

ACAb

B0

00

000

4minus0003

115293

IMCV

KKILDK

Hom

osapiens

Bostaurus

DEW

EDWI

115295

INPS

KEN

LCST

FCK

EVVRN

AHom

osapiens

Bostaurus

OOA

FIAminus003minus003minus003minus002minus0029

65105

TLTP

ENTL

Mus

musculus

Shigellaflexneri

AID

ELISA

65110

TLTS

GSD

LDRC

TTFD

DV

Oryctolagus

cuniculus

SARS

coronavirus

AID

ELISA

001

0013minus0003

001

001

1344

71PK

PEQ

Mus

musculus

Streptococcus

pneumoniae

AID

FACS

1344

72PL

LPGT

STTS

TGPC

KTHom

osapiens

HepatitisB

virus

AID

ELISA

002

0018

0019

002

0016

Journal of Immunology Research 11Ta

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

142228

LYCY

EQLN

DSSE

Hom

osapiens

Hum

anpapillomaviru

sNI

ELISA

142250

NWGDEP

SKRR

DRS

NSR

GRK

NFelis

catus

Felin

einfectio

usAID

ELISA

007

006

80071

0073

007

21549

GNYD

FWYQ

SHom

osapiens

Staphylococcus

aureus

OOID

ELISA

21553

GNYN

YKY

RYLR

HGK

LRPF

ERMus

musculus

SARS

coronavirus

AID

ELISA

003

0032

0021

0031

003

21549

GNYD

FWYQ

SHom

osapiens

Staphylococcus

aureus

OOID

ELISA

21553

GNYN

YKY

RYLR

HGK

LRPF

EROryctolagus

cuniculus

SARS

coronavirus

AID

ELISA

003

0034

0021

0031

003

34908

LAPL

GE

Hom

osapiens

HepatitisE

virus

EWEIR

ELISA

34914

LAPS

TLRS

LRKR

RLSSP

Hom

osapiens

Hum

anherpesvirus

OOID

ELISA

006

006

0059

0063

006

130454

CLFP

NNSY

CMus

musculus

MD

AID

NIAA

130456

CRPQ

VN

NPK

EWS

CAAC

Hom

osapiens

MD

OOD

ACAb

B001

0008

001

0011

0007

1964

4GFV

PSM

Hom

osapiens

Hepatitisd

elta

virus

OOID

ELISA

1964

7GFV

SASI

FGFQ

AEV

GPN

NTR

Oryctolagus

cuniculus

VacciniavirusW

RAID

ELISAminus001minus000

6minus0013minus000

9minus001

20678

GKRP

EMus

musculus

Streptococcus

pyogenes

AID

WI

20680

GKS

KRD

AKN

NAAK

LAVDKL

LMus

musculus

VacciniavirusW

RAID

WI

001

001

0001

001

001

123278

GYL

KDLP

TTOvisa

ries

Fasciolahepatica

AID

ELISA

123282

HAC

QKK

LLKF

EAL

QQEE

GEE

Rattu

snorvegicu

sGallusgallus

AID

PFFminus002minus0016minus0016minus002minus0025

141067

PLSLEP

DP

Mus

musculus

Hom

osapiens

AID

FACS

141073

REGVRW

RVMAIQ

Mus

musculus

Hom

osapiens

AID

ELISA

006

006

006

006

0056

139248

SFAG

TVIE

Mus

musculus

Classicalsw

ine

AID

WI

139305

TAAQ

ITQ

RKWEA

ARE

AEQ

RROryctolagus

cuniculus

Hom

osapiens

AID

IP003

0033

0026

003

003

104515

HDCR

PKKI

Mus

musculus

LaCrossevirus

AID

IFAIH

104520

IAKE

QE

NKE

TIGT

LKKI

LDE

Rattu

snorvegicu

sStreptococcus

pyogenes

AID

ELISAminus006minus006minus0073minus006minus0061

113517

HLY

ADGLT

DMus

musculus

Hum

anpapillomaviru

sAID

IFAIH

113518

HNKI

QA

IELE

DLL

RYS

KLYR

Mus

musculus

Hom

osapiens

AID

ELISA

002

002

0011

002

0019

156970

VER

HQ

Hom

osapiens

Hom

osapiens

OAID

ELISA

156975

WSSTV

LRVS

PTRT

VP

Mus

musculus

MD

AID

ELISA

001

0012

0003

0017

001

78252

PVQNLT

Mus

musculus

Porphyromonas

gingivalis

AID

WI

78253

QGGCG

RGWAFS

ATG

AIEA

Mus

musculus

Glycinem

axAID

ELISA

002

002

0017

002

0018

6068

CCPD

KNKS

Mus

musculus

Hum

anherpesvirus

AID

WI

6074

CCRH

KQKD

VGDVK

QTL

PPS

Ovisa

ries

MD

AID

ELISA

001

000

6000

4001

0008

12 Journal of Immunology Research

Table3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

53109

RAGVC

YHom

osapiens

Triticum

aestivum

OOA

ELISA

53116

RAILTA

FSPA

QDI

WGTS

Oryctolagus

cuniculus

SARS

coronavirus

AID

ELISAminus002minus0016minus0037minus0023minus002

147041

IPEQ

Hom

osapiens

Triticum

aestivum

OOA

WI

147064

KHQGA

QYV

WN

RTA

Hom

osapiens

Bostaurus

OOID

WI

006

006

0047

0057

006

7066

4VQ

VVYD

YQOryctolagus

cuniculus

Treponem

apallidu

mAID

ELISA

70667

VQWMN

RLIAFA

FAG

NHVS

PHom

osapiens

HepatitisC

virus

OOID

ELISA

005

004

6004

10049

005

134343

DLY

IKMus

musculus

Hum

anpapillomaviru

sAID

PhDIP

134344

DMAQ

VTV

GPG

LLGVS

TLMus

musculus

Hom

osapiens

AID

PhDIP

00minus000

90

0

Journal of Immunology Research 13

The first input term is the value 120582(120576119894119895)ref is the scoring

function 120582 of the efficiency of the initial process 120576119894119895(known

solution) The function 120582(120576119894119895)ref = 1 if the 119894th peptide could

experimentally be demonstrated to be a B-epitope in theassay of reference (reference) carried out in the conditions119888119895 120582(120576119894119895)ref = 0 otherwise The variational-perturbation

terms ΔΔ120594119888119895are at the same time terms typical of pertur-

bation theory and moving average (MA) functions used inBox-Jenkin models in time series [60] These new types ofterms account both for the deviation of the electronegativityof all amino acids in the sequence of the new peptidewith respect to the peptide of reference and with respectto all boundary conditions In Table 1 we give the overallclassification results obtainedwith thismodel Speck-Plancheet al [61ndash63] introduced different multitargetmultiplexingQSARmodels that incorporate this type of information basedon MAs The results obtained with the present model areexcellent comparedwith other similarmodels in the literatureuseful for other problems including moving average models[64 65] or perturbation models [58] Notably this is also thefirst model combining both perturbation theory and MAs ina QSPR context

The other input terms are the following The first Δ120594seq =120594(119898119902)ref minus120594(119898119894)new is the perturbation term for the variation

or in themean value of electronegativity for all amino acids inthe sequence of the peptide of reference The remnant inputvariables of the model ΔΔ120594

119888119895= Δ120594

119888119895-ref minus Δ120594119888119895-new =

[120594(119898119902)ref minus

lowast120594 (119888119902119903)ref] minus [120594(119898119894)new minus

lowast120594 (119888119894119895)new] quantify

values of the conditions of the new assay 119888119895-new that representperturbations with respect to the initial conditions 119888

119894119895-ref of

the assay of reference The quantities lowast120594(119888119894119895) and lowast120594(119888

119902119903) are

the average values of the mean electronegativity values 120594(119898119894)

and 120594(119898119902) for all new and reference peptides in IEDB that

are epitopes under the 119895th or 119903th boundary condition Thevalues of these terms have been tabulated for gt500 sourceorganisms gt50 host organisms gt10 biological process andgt30 experimental techniques We must substitute the valuesof 120594(119898

119894) and 120594(119898

119902) of the new and reference peptides and the

tabulated values of lowast120594(119888119894119895) and lowast120594(119888

119902119903) for all combinations

of boundary conditions to predict the perturbations of theaction as epitope of peptides In doing so we can found theoptimal sequence and boundary conditions towards the useof the peptide in the development of a vaccine In Table 2 wegive some of these values of lowast120594(119888

119894119895) and lowast120594(119888

119902119903)

In Table 3 we depict the sequences and input-outputboundary conditions for top perturbations present in IEDBAll these perturbations have observed value of 120582(120576

119894119895)new = 1

and predicted value also equal to 1 with a high probabilitySee Supplementary Material available online at httpdxdoiorg1011552014768515 file contains a full list of gt200000cases of perturbations

4 Conclusions

It is possible to develop generalmodels for vaccine design ableto predict the results of multiple input-output perturbations

in peptide sequence and experimental assay boundary condi-tions using ideas of QSPR analysis perturbation theory andBox and Jenkins MA operators The electronegativity valuescalculated withMARCH-INSIDE seem to be goodmoleculardescriptors for this type of QSPR-perturbation models

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

The present study was partially supported by GrantsAGL2010-22290-C02 and AGL2011-30563-C03 from Minis-terio de Ciencia e Innovacion Spain and Grant CN 2012155from Xunta de Galicia Spain

References

[1] B Peters J Sidney P Bourne et al ldquoThe design and implemen-tation of the immune epitope database and analysis resourcerdquoImmunogenetics vol 57 no 5 pp 326ndash336 2005

[2] B Peters J Sidney P Bourne et al ldquoThe immune epitopedatabase and analysis resource from vision to blueprintrdquo PLoSBiology vol 3 no 3 p e91 2005

[3] PWang A AMorgan Q Zhang A Sette and B Peters ldquoAuto-mating document classification for the Immune Epitope Data-baserdquo BMC Bioinformatics vol 8 article 269 2007

[4] A Sette ldquoThe immune epitope database and analysis resourcefrom vision to blueprintrdquo Genome Informatics vol 15 no 2 p299 2004

[5] N Salimi W Fleri B Peters and A Sette ldquoThe immune epi-tope database a historical retrospective of the first decaderdquoImmunology vol 137 no 2 pp 117ndash123 2012

[6] Y Kim A Sette and B Peters ldquoApplications for T-cell epitopequeries and tools in the Immune EpitopeDatabase andAnalysisResourcerdquo Journal of Immunological Methods vol 374 no 1-2pp 62ndash69 2011

[7] Y Kim J Ponomarenko Z Zhu D Tamang et al ldquoImmuneepitope database analysis resourcerdquo Nucleic Acids Research ppW525ndashW530 2012

[8] A M Helguera R D Combes M P Gonzalez and M N DS Cordeiro ldquoApplications of 2D descriptors in drug design aDRAGON talerdquo Current Topics in Medicinal Chemistry vol 8no 18 pp 1628ndash1655 2008

[9] GM Casanola-Martın Y Marrero-Ponce M T H Khan et alldquoDragon method for finding novel tyrosinase inhibitors Biosil-ico identification and experimental in vitro assaysrdquo The Euro-pean Journal of Medicinal Chemistry vol 42 no 11-12 pp 1370ndash1381 2007

[10] I V Tetko J Gasteiger R Todeschini et al ldquoVirtual computa-tional chemistry laboratorymdashdesign and descriptionrdquo Journalof Computer-AidedMolecular Design vol 19 no 6 pp 453ndash4632005

[11] A R Katritzky A Oliferenko A Lomaka and M KarelsonldquoSix-membered cyclic ureas as HIV-1 protease inhibitors aQSAR study based on CODESSA PRO approachrdquo Bioorganicand Medicinal Chemistry Letters vol 12 no 23 pp 3453ndash34572002

14 Journal of Immunology Research

[12] A R Katritzky S Perumal R Petrukhin and E Klein-peter ldquoCODESSA-based theoretical QSPRmodel for hydantoinHPLC-RT lipophilicitiesrdquo Journal of Chemical Information andComputer Sciences vol 41 no 3 pp 569ndash574 2001

[13] S Vilar G Cozza and S Moro ldquoMedicinal chemistry and themolecular operating environment (MOE) application of QSARand molecular docking to drug discoveryrdquo Current Topics inMedicinal Chemistry vol 8 no 18 pp 1555ndash1572 2008

[14] G Marzaro A Chilin A Guiotto et al ldquoUsing the TOPS-MODE approach to fit multi-target QSAR models for tyrosinekinases inhibitorsrdquo The European Journal of Medicinal Chem-istry vol 46 no 6 pp 2185ndash2192 2011

[15] S Vilar E Estrada E Uriarte L Santana and Y Gutierrez ldquoInsilico studies toward the discovery of new anti-HIV nucleosidecompounds through the use of tops-mode and 2D3D con-nectivity indices 2 Purine derivativesrdquo Journal of ChemicalInformation and Modeling vol 45 no 2 pp 502ndash514 2005

[16] E Estrada J A Quincoces and G Patlewicz ldquoCreating molec-ular diversity from antioxidants in Brazilian propolis combina-tion of TOPS-MODE QSAR and virtual structure generationrdquoMolecular Diversity vol 8 no 1 pp 21ndash33 2004

[17] E Estrada andHGonzalez ldquoWhat are the limits of applicabilityfor graph theoretic descriptors in QSPRQSAR modelingdipole moments of aromatic compounds with TOPS-MODEdescriptorsrdquo Journal of Chemical Information and ComputerSciences vol 43 no 1 pp 75ndash84 2003

[18] Y Marrero-Ponce J A Castillo-Garit E Olazabal et al ldquoTom-ocomd-Cardd a novel approach for computer-aided ldquorationalrdquodrug design I Theoretical and experimental assessment of apromising method for computational screening and in silicodesign of new anthelmintic compoundsrdquo Journal of Computer-Aided Molecular Design vol 18 no 10 pp 615ndash634 2004

[19] Y Marrero Ponce R Medina Marrero E A Castro et al ldquoPro-tein quadratic indices of the ldquomacromolecular pseudographrsquos 120572-carbon atom adjacency matrixrdquo 1 prediction of arc repressoralanine-mutantrsquos stabilityrdquoMolecules vol 9 no 12 pp 1124ndash11472004

[20] H Gonzalez-Dıaz F Prado-Prado and F M Ubeira ldquoPredict-ing antimicrobial drugs and targets with the MARCH-INSIDEapproachrdquo Current Topics in Medicinal Chemistry vol 8 no 18pp 1676ndash1690 2008

[21] R Garcıa-Domenech J Galvez J V de Julian-Ortiz and LPogliani ldquoSomenew trends in chemical graph theoryrdquoChemicalReviews vol 108 no 3 pp 1127ndash1169 2008

[22] E Estrada and E Uriarte ldquoRecent advances on the role of topo-logical indices in drug discovery researchrdquo Current MedicinalChemistry vol 8 no 13 pp 1573ndash1588 2001

[23] R Todeschini andVConsonniHandbook ofMolecularDescrip-tors Wiley-VCH Weinheim Germany 2008

[24] E Estrada E J Delgado J B Alderete and G A Jana ldquoQuan-tum-connectivity descriptors inmodeling solubility of environ-mentally important organic compoundsrdquo Journal of Computa-tional Chemistry vol 25 no 14 pp 1787ndash1796 2004

[25] E Besalu X Girones L Amat andR Carbo-Dorca ldquoMolecularquantum similarity and the fundamentals of QSARrdquo Accountsof Chemical Research vol 35 no 5 pp 289ndash295 2002

[26] D A Rincon M N D S Cordeiro and R A Mosquera ldquoOnthe electronic structure of cocaine and its metabolitesrdquo Journalof Physical Chemistry A vol 113 no 50 pp 13937ndash13942 2009

[27] M Mandado M J Gonzalez-Moa and R A MosqueraldquoChemical graph theory and n-center electron delocalization

indices a study on polycyclic aromatic hydrocarbonsrdquo Journalof Computational Chemistry vol 28 no 10 pp 1625ndash1633 2007

[28] THill andP Lewicki STATISTICSMethods andApplications AComprehensive Reference for Science Industry and DataMiningStatSoft Tulsa Okla USA 2006

[29] E Frank M Hall L Trigg G Holmes and I H Witten ldquoDatamining in bioinformatics using Wekardquo Bioinformatics vol 20no 15 pp 2479ndash2481 2004

[30] G Patlewicz N Ball E D Booth E Hulzebos E Zvinavasheand C Hennes ldquoUse of category approaches read-across and(Q)SAR general considerationsrdquo Regulatory Toxicology andPharmacology vol 67 no 1 pp 1ndash12 2013

[31] D W Roberts and G Y Patlewicz ldquoUpdating the skin sensiti-zation in vitro data assessment paradigm in 2009mdasha chemistryand QSAR perspectiverdquo Journal of Applied Toxicology vol 30no 3 pp 286ndash288 2010

[32] D W Roberts and G Y Patlewicz ldquoNonanimal alternatives forskin sensitization letter to the editorrdquoToxicological Sciences vol106 no 2 pp 572ndash574 2008

[33] E Estrada G Patlewicz and Y Gutierrez ldquoFrom knowledgegeneration to knowledge archive a general strategy usingTOPS-MODE with DEREK to formulate new alerts for skinsensitizationrdquo Journal of Chemical Information and ComputerSciences vol 44 no 2 pp 688ndash698 2004

[34] G F Gerberick C A Ryan P S Kern et al ldquoA chemical datasetfor evaluation of alternative approaches to skin-sensitizationtestingrdquo Contact Dermatitis vol 50 no 5 pp 274ndash288 2004

[35] G Y Patlewicz D A Basketter C K Smith Pease et al ldquoFurtherevaluation of quantitative structurendashactivity relationship mod-els for the prediction of the skin sensitization potency ofselected fragrance allergensrdquo Contact Dermatitis vol 50 no 2pp 91ndash97 2004

[36] E Estrada G Patlewicz M Chamberlain D Basketter andS Larbey ldquoComputer-aided knowledge generation for under-standing skin sensitization mechanisms the TOPS-MODEapproachrdquo Chemical Research in Toxicology vol 16 no 10 pp1226ndash1235 2003

[37] G Patlewicz R Rodford and J D Walker ldquoQuantitative struc-ture-activity relationships for predicting skin and eye irritationrdquoEnvironmental Toxicology and Chemistry vol 22 no 8 pp1862ndash1869 2003

[38] G Y Patlewicz R A Rodford G Ellis and M D Barratt ldquoAQSAR model for the eye irritation of cationic surfactantsrdquo Tox-icology In Vitro vol 14 no 1 pp 79ndash84 2000

[39] E Tenorio-Borroto C G Penuelas Rivas J C VasquezChagoyan N Castanedo F J Prado-Prado and X Garcia-Mera ldquoANN multiplexing model of drugs effect on macro-phages theoretical and flow cytometry study on the cyto-toxicity of the anti-microbial drug G1 in spleenrdquo Bioorganic ampMedicinal Chemistry vol 20 no 20 pp 6181ndash6194 2012

[40] D R Flower H McSparron M J Blythe et al ldquoComputationalvaccinology quantitative approachesrdquo Novartis FoundationSymposium vol 254 pp 102ndash120 2003

[41] I A Doytchinova P Guan and D R Flower ldquoQuantitativestructure-activity relationships and the prediction of MHCsupermotifsrdquoMethods vol 34 no 4 pp 444ndash453 2004

[42] Y Xiao and M R Segal ldquoPrediction of genomewide conservedepitope profiles of HIV-1 classifier choice and peptide rep-resentationrdquo Statistical Applications in Genetics and MolecularBiology vol 4 no 1 article 25 2005

Journal of Immunology Research 15

[43] T Fagerberg V Zoete S Viatte P Baumgaertner P M Alves PRomero et al ldquoPrediction of cross-recognition of peptide-HLAA2 by melan-a-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationshipsrdquo PLoSONE vol 8 no 7 Article ID e65590 2013

[44] D Barh A N Misra A Kumar and A Vasco ldquoA novel strategyof epitope design inNeisseria gonorrhoeaerdquo Bioinformation vol5 no 2 pp 77ndash82 2010

[45] J Bi R Song H Yang et al ldquoStepwise identification of HLA-Alowast0201-restricted CD8+ T-cell epitope peptides from herpessimplex virus type 1 genome boosted by a steprank schemerdquoBiopolymers vol 96 no 3 pp 328ndash339 2011

[46] R D Bremel and E J Homan ldquoAn integrated approach to epit-ope analysis II a system for proteomic-scale prediction ofimmunological characteristicsrdquo Immunome Research vol 6 no1 article 8 2010

[47] C M Diez-Rivero B Chenlo P Zuluaga and P A RecheldquoQuantitative modeling of peptide binding to TAP using sup-port vector machinerdquo Proteins vol 78 no 1 pp 63ndash72 2010

[48] E Martınez-Naves E M Lafuente and P A Reche ldquoRecogni-tion of the ligand-type specificity of classical and non-classicalMHC I proteinsrdquo FEBS Letters vol 585 no 21 pp 3478ndash34842011

[49] M Bhasin E L Reinherz and P A Reche ldquoRecognition andclassification of histones using support vectormachinerdquo Journalof Computational Biology vol 13 no 1 pp 102ndash112 2006

[50] P A Reche and E L Reinherz ldquoPEPVAC a web server formulti-epitope vaccine development based on the prediction ofsupertypic MHC ligandsrdquo Nucleic Acids Research vol 33 no 2pp W138ndashW142 2005

[51] E M Lafuente and P A Reche ldquoPrediction of MHC-peptidebinding a systematic and comprehensive overviewrdquo CurrentPharmaceutical Design vol 15 no 28 pp 3209ndash3220 2009

[52] A Gaulton L J Bellis A P Bento et al et al ldquoA large-scalebioactivity database for drug discoveryrdquoNucleic Acids Researchpp D1100ndashD1107 2012

[53] W H Cropper Great Physicists The Life and Times of LeadingPhysicists from Galileo to Hawking Oxford University PressNew York NY USA 2004

[54] E L Bouzarth A Brooks R Camassa et al ldquoEpicyclic orbits ina viscous fluid about a precessing rod theory and experimentsat the micro-and macro-scalesrdquo Physical Review E vol 76 no1 part 2 Article ID 016313 2007

[55] M Laberge ldquoIntrinsic protein electric fields basic non-covalentinteractions and relationship to protein-induced Stark effectsrdquoBiochimica et BiophysicaActa vol 1386 no 2 pp 305ndash330 1998

[56] FM Fernandez and J AMorales ldquoPerturbation theorywithoutwave functions for the Zeeman effect in hydrogenrdquo PhysicalReview A vol 46 no 1 pp 318ndash326 1992

[57] B D Marshall and W G Chapman ldquoThermodynamic pertur-bation theory for associating fluids with small bond angleseffects of steric hindrance ring formation and double bondingrdquoPhysical Reiew E vol 87 no 5 Article ID 052307 12 pages 2013

[58] H Gonzalez-Diaz S Arrasate A Gomez-San Juan et al ldquoNewtheory for multiple input-output perturbations in complexmolecular systems 1 Linear QSPR electronegativity models inphysical organic and medicinal chemistryrdquo Current Topics inMedicinal Chemistry 2013

[59] H Gonzalez-Diaz S Arrasate N Sotomayor et al ldquoMIANNmodels in medicinal physical and organic chemistryrdquo CurrentTopics in Medicinal Chemistry vol 13 no 5 pp 619ndash641 2013

[60] G E P Box and GM Jenkins Time Series Analysis Forecastingand Control Holden-Day San Francisco Calif USA 1970

[61] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoUnified multi-target approach for the rational insilico design of anti-bladder cancer agentsrdquo Anti-Cancer Agentsin Medicial Chemistry vol 13 no 5 pp 791ndash800 2013

[62] A Speck-Planche V V Kleandrova and M N Cordeiro ldquoNewinsights toward the discovery of antibacterial agents multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugsrdquo TheEuropean Journal of Pharmaceutical Sciences vol 48 no 4-5pp 812ndash818 2013

[63] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoMulti-target inhibitors for proteins associated withAlzheimer in silico discovery using fragment-based descrip-torsrdquo Current Alzheimer Research vol 10 no 2 pp 117ndash1242013

[64] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoIn silico discovery and virtual screening of multi-target inhibitors for proteins in Mycobacterium tuberculosisrdquoCombinatorial Chemistry amp High Throughput Screening vol 15no 8 pp 666ndash673 2012

[65] A Speck-Planche V V Kleandrova F Luan and M N DS Cordeiro ldquoChemoinformatics in anti-cancer chemotherapymulti-target QSAR model for the in silico discovery of anti-breast cancer agentsrdquo The European Journal of PharmaceuticalSciences vol 47 no 1 pp 273ndash279 2012

Submit your manuscripts athttpwwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MEDIATORSINFLAMMATION

of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Behavioural Neurology

EndocrinologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Disease Markers

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

OncologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Oxidative Medicine and Cellular Longevity

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational and Mathematical Methods in Medicine

OphthalmologyJournal of

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Diabetes ResearchJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Research and TreatmentAIDS

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Gastroenterology Research and Practice

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Page 6: Research Article Model for Vaccine Design by …downloads.hindawi.com/journals/jir/2014/768515.pdfResearch Article Model for Vaccine Design by Prediction of B-Epitopes of IEDB Given

6 Journal of Immunology Research

Table3To

p100

values

ofp1

forp

ositive

perturbatio

nsin

training

serie

sNew

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

115153

MKG

VVC

TRIYEK

VHom

osapiens

Hom

osapiens

OAID

ELISA

115155

NNQRK

KAKN

TPFN

MLK

RERN

Mus

musculus

Denguev

irus2

AID

WI

001

0012

000

40017

0012

52124

QQQPP

Hom

osapiens

Triticum

aestivum

OOA

ELISA

52128

QQQQGG

SQSQ

KGKQ

QHom

osapiens

Glycinem

axOOA

WI

00minus0018

00002

3639

APL

GVT

Hom

osapiens

HepatitisE

virus

EWEIR

ELISA

3652

APL

TRG

SCRK

RNRS

PER

Hom

osapiens

Hum

anherpesvirus

OOID

ELISA

004

004

0039

0043

004

135959

LTRA

YAKD

VKF

GHom

osapiens

Hom

osapiens

OAID

ELISA

135963

NGQEE

KAG

VVS

TGLIGGG

Mus

musculus

MD

AID

ELISAminus004minus0038minus0047minus0033minus004

108075

PREP

QVY

Hom

osapiens

Hom

osapiens

OAID

ELISA

108078

PTSP

SGV

EEWIV

TQVVPG

VAOryctolagus

cuniculus

Hom

osapiens

AID

ACAb

Bminus001minus000

6minus001minus0003minus0011

25113

HVVDLP

Hom

osapiens

HepatitisE

virus

EWEIR

ELISA

25126

HWGNH

SKSH

PQR

Mus

musculus

MD

AID

ELISA

002

0022

0013

0023

002

48780

PPFSPQ

Hom

osapiens

Hepatitisd

elta

virus

OOID

ELISA

48782

PPFT

SAV

GGVDHRS

Mus

musculus

MD

AID

SAC

002

0022

0008

0021

0021

40988

LYVVA

YQA

Mus

musculus

Viscum

album

AID

ELISA

4100

4MAARL

CCQLD

PARD

VHom

osapiens

HepatitisB

virus

OOID

ELISA

002

0018

000

60019

002

50439

QDAY

NAAG

Mus

musculus

Mycobacteriu

mscrofulaceum

AID

ELISA

5044

5QDCN

CSI

YPGHAS

GHRM

AWD

Hom

osapiens

HepatitisC

virus

OOID

ELISA

004

0038

0028

0039

004

98849

KIPA

VFK

IDA

Hom

osapiens

Bostaurus

DEW

EDWI

98850

KKGSE

EEGDITNPIN

Hom

osapiens

Arachish

ypogaea

OOA

IFAIHminus005minus005minus0053minus004minus0051

116171

TQDQDP

BBHFF

KNIV

TPR

Hom

osapiens

Hom

osapiens

OAID

ACAb

B116

286

CGKG

LSAT

VTG

GQKG

RGSR

Oryctolagus

cuniculus

Mus

musculus

AID

MS

001

0014

0008

0017

000

9

123442

LLKD

LRKN

Hom

osapiens

Bornadiseasev

irus

EWEIR

WI

123443

LLTE

HR

MTW

DPA

QPP

RDLT

EHom

osapiens

Hom

osapiens

OOID

ELISAminus002minus002minus0025minus0017minus0022

47858

PHVVDL

Hom

osapiens

HepatitisE

virus

EWEIR

ELISA

47860

PHWIK

KPNRQ

GLG

YYS

Caprahircus

Hum

anT-lymphotropic

virus

AID

ACAb

B001

0007

0005

0013

000

9

61783

STNKA

VVS

LSBo

staurus

Bovine

respira

tory

AID

ELISA

61792

STNPK

PQRK

TKRN

TNRR

PQD

Hom

osapiens

HepatitisC

virus

OOID

ELISA

003

0025

0019

0029

003

118210

VMLY

QISEE

Hom

osapiens

Hom

osapiens

OAID

WI

118217

VTK

YITK

GWKE

VH

Oryctolagus

cuniculus

Hom

osapiens

AID

ELISA

005

0054

005

0057

004

8

130944

LFKH

SOryctolagus

cuniculus

Rattu

snorvegicus

AID

ELISA

130956

LPPR

VTP

KWSLDA

WST

WR

Hom

osapiens

MD

OOD

WI

001

000

6minus0002

0011

0012

Journal of Immunology Research 7Ta

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

23028

GVKY

AHom

osapiens

MD

OAID

WI

23032

GVLA

KDVRF

SQV

Hom

osapiens

MD

OOID

ELISA

00

00007minus0002

51199

QKK

AIE

Oryctolagus

cuniculus

Vibriocholerae

AID

ELISA

51204

QKK

NK

RNTN

RRPQ

DV

Hom

osapiens

HepatitisC

virus

OOID

ELISA

003

0026

0018

0029

003

144783

SHVVT

MLD

NF

Hom

osapiens

Hom

osapiens

NI

ELISA

144786

SMNRG

RGTH

PSLIWM

Mus

musculus

MD

AID

ACAb

B003

0032

0023

0033

0029

134343

DLY

IKMus

musculus

Hum

anpapillomaviru

sAID

NIAA

134344

DMAQ

VTV

GPG

LLGVS

TLMus

musculus

Hom

osapiens

AID

WI

00minus000

90

0

38321

LNQLA

GRM

Anas

platyrhynchos

Duckhepatitis

AID

ELISA

38323

LNQTA

RAFP

DCA

ICW

EPSP

POryctolagus

cuniculus

Bovine

leukemia

virus

AID

ACAb

Bminus001minus0008minus0022minus001minus0011

144657

GQITVD

MMYG

Hom

osapiens

Hom

osapiens

OAID

ELISA

1446

61GRE

GYP

ADGGCA

WPA

CYC

Oryctolagus

cuniculus

MD

AID

WI

002

0024

0013

0027

0022

21084

GLQ

NMus

musculus

Chlamydia

trachom

atis

AID

ELISA

21093

GLR

AQD

DFS

GWDI

NTP

AFE

WMus

musculus

Mycobacteriu

mtuberculosis

AID

WI

003

003

0013

003

0032

98453

SGFS

GSV

QFV

Oryctolagus

cuniculus

Neisseria

meningitid

isAID

ELISA

98456

SICS

NN

PTCW

AIC

KRIPNKK

Mus

musculus

Hum

anrespira

tory

virus

AID

IFAIH

004

0037

0027

004

004

1

98453

SGFS

GSV

QFV

Mus

musculus

Neisseria

meningitid

isAID

ELISA

98456

SICS

NN

PTCW

AIC

KRIPNKK

Mus

musculus

Hum

anrespira

tory

virus

AID

IFAIH

004

004

0027

004

004

1

107107

EAIQ

PRa

ttus

norvegicu

sHom

osapiens

AID

ELISA

107110

EKER

RPSP

IGTA

TLL

Hom

osapiens

MD

OOA

ELISA

005

004

80043

0053

005

110857

FTGEA

YSY

WSA

KHom

osapiens

Mycoplasm

apenetra

nsEW

EIR

ELISA

110859

GEE

SRIS

LPLP

NF

SSLN

LRE

Mus

musculus

Hom

osapiens

AID

FIA

00002minus0017

0003

0003

36315

LGSA

YPMus

musculus

Mycobacteriu

mlep

rae

MD

ELISA

36317

LGSG

AFG

TIYK

GMus

musculus

Avian

erythroblasto

sisvirus

AID

ACAb

B001

001

00011

000

9

122034

WNPA

DRa

ttus

norvegicu

sTorpedocalifornica

AID

ELISA

122035

WNPA

DYG

GIK

WN

PADYG

GIK

Rattu

snorvegicu

sMD

AID

RIA

001

001

0001

001

000

9

25013

HVA

DID

KLID

Mus

musculus

Puum

alavirus

Kazan

AID

ELISA

25021

HVA

PTH

YVTE

SDA

SQRV

TQL

Hom

osapiens

HepatitisC

virus

OOID

ELISA

0minus0002minus0014minus0001

0

36162

LGIH

EOryctolagus

cuniculus

Cand

idaalbicans

AID

ELISA

36166

LGIM

GE

YRGTP

RNQDLY

DAA

Mus

musculus

Hum

anrespira

tory

virus

AID

RIA

0minus0003minus0007

0minus0001

67253

TWEV

LHMus

musculus

Plasmodium

vivax

AID

ELISA

67257

TWGEN

ETDVLL

LNNTR

PPQ

Hom

osapiens

HepatitisC

virus

OOID

ACAb

Bminus002minus0022minus0027minus0021minus0021

8 Journal of Immunology Research

Table3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

50990

QGYR

VSSY

LPHom

osapiens

Hevea

brasiliensis

OOA

WI

50998

QHEQ

DR

PTPS

PAP

SRPF

SVL

Hom

osapiens

HepatitisE

virus

OOID

ELISA

001

001minus0002

0007

0008

100458

RDVLQ

LYAPE

Mus

musculus

Bacillusa

nthracis

AID

ELISA

1004

62RF

STRY

GNQNGRI

RVLQ

RFD

Hom

osapiens

Arachish

ypogaea

EWED

ELISA

003

0028

0018

003

003

111036

TEST

FTGEA

YSV

Hom

osapiens

Mycoplasm

apenetra

nsEW

EIR

ELISA

111039

TGVPID

PAVPD

SSIV

PLLE

SBo

staurus

Bovine

papillomaviru

sAID

ELISA

003

0035

002

0033

003

117919

IFIEME

Hom

osapiens

Hom

osapiens

OAID

WI

117921

IGIIDLIE

KRKF

NQ

Mus

musculus

Hom

osapiens

AID

WI

003

0032

003

0037

003

7127

CTDTD

KLF

Oryctolagus

cuniculus

Shigellaflexneri

AID

ELISA

7128

CTDVS

TAIH

ADQL

TPAW

Hom

osapiens

SARS

coronavirus

OOID

ELISA

001

000

6minus0003

000

9001

112253

PGQSP

KLHom

osapiens

Hom

osapiens

OAID

ELISA

112255

PIRA

LVGDEV

ELP

CRISPG

KMus

musculus

Hom

osapiens

AID

ELISA

001

0012

001

0017

001

122034

WNPA

DRa

ttus

norvegicu

sTorpedocalifornica

AID

ELISA

122038

WNPD

DY

GGVKW

NP

DDYG

GVK

Rattu

snorvegicu

sMD

AID

RIA

00minus000

90minus0001

131878

FLMLV

GGST

LHom

osapiens

Hom

osapiens

OAID

ACAb

B131879

FLVA

HT

RARA

PSA

GER

ARR

SMus

musculus

Mus

musculus

AID

NIAA

003

0032

0028

0037

0033

7066

4VQ

VVYD

YQHom

osapiens

Treponem

apallidu

mOOID

ELISA

70667

VQWMNR

LIAFA

FAG

NHVS

PHom

osapiens

HepatitisC

virus

OOID

ELISA

005

005

004

1005

005

71545

VTV

Hom

osapiens

Helicobacterpylori

OOID

ELISA

71559

VTV

RGGL

RILSPD

RKHom

osapiens

Arachish

ypogaea

OOA

WI

004

004

0032

0043

004

2

127856

TDVRY

KD

Mus

musculus

Mus

musculus

AID

ACAb

B127857

TDVRY

KDDMYH

FFCP

AIQ

AQMus

musculus

Mus

musculus

AID

PFF

001

001

001

001

000

6

112149

GVG

WIRQ

Hom

osapiens

Hom

osapiens

OAID

ELISA

112152

HHPA

RTAHYG

SLP

QKS

HGRT

Hom

osapiens

Hom

osapiens

AID

ELISA

00

00007

0

119581

FSCS

VMHE

Hom

osapiens

Hom

osapiens

OAID

ELISA

119582

GLQ

LIQL

INVDEV

NQI

Mus

musculus

Hom

osapiens

AID

RIAminus001minus0008minus001minus0003minus0011

144657

GQITVD

MMYG

Hom

osapiens

Hom

osapiens

OAID

ELISA

144659

GRE

GYP

ADGGAA

GYC

NTE

Oryctolagus

cuniculus

MD

AID

WIminus001minus000

6minus0017minus0003minus0008

25013

HVA

DI

DKL

IDMus

musculus

Puum

alavirus

Kazan

AID

ELISA

25022

HVA

PTH

YVVES

DA

SQRV

TQV

Hom

osapiens

HepatitisC

virus

OOID

ELISA

0minus0002minus0014minus0001

0

Journal of Immunology Research 9Ta

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

144652

GMRG

MKG

LVY

Hom

osapiens

Hom

osapiens

OAID

ELISA

144654

GPH

PTLE

VVPM

GRG

SMus

musculus

MD

AID

ELISAminus002minus0018minus0027minus0013minus002

104515

HDCR

PKKI

Mus

musculus

LaCrossevirus

AID

IFAIH

104521

IGTL

KKIL

DET

VKD

KIAKE

QRa

ttus

norvegicu

sStreptococcus

pyogenes

AID

ELISAminus004minus004minus0053minus004minus004

1

7367

CYGDWA

Hom

osapiens

Triticum

aestivum

OOA

ELISA

7374

CYGLP

DS

EPTK

TNGK

Mus

musculus

Tityus

serrulatus

AID

WIminus002minus0018minus0043minus0023minus0018

7367

CYGDWA

Hom

osapiens

Triticum

aestivum

OOA

ELISA

7374

CYGLP

DS

EPTK

TNGK

Mus

musculus

Tityus

serrulatus

AID

WIminus002minus0018minus0043minus0023minus0018

144610

DFF

TYK

Mus

musculus

Porcine

transmissible

AID

WI

144611

DFN

GSF

DMNGTITA

Oryctolagus

cuniculus

Escherich

iacoli

AID

ELISAminus001minus0007minus0018minus001minus0012

112047

AST

RESG

Hom

osapiens

Hom

osapiens

OAID

ELISA

112048

ATAST

MDHARH

GF

LPRH

RDT

Hom

osapiens

Hom

osapiens

AID

ELISA

005

005

005

0057

005

36136

LGGVFT

Hom

osapiens

Denguev

irus2

OOID

ELISA

36137

LGGWKL

QSD

PRAY

AL

Hom

osapiens

Ambrosia

artemisiifolia

OOA

RIA

001

001

0007

0013

000

9

115256

FREL

KDLK

GY

Hom

osapiens

Bostaurus

DEW

EDWI

115261

GDLE

ILL

QKW

ENG

ECAQ

KKI

Hom

osapiens

Bostaurus

OOA

FIAminus001minus001minus001

0minus000

9

129024

KADQLY

KHom

osapiens

Hom

osapiens

OAID

ELISA

129026

KAKK

PAAAAG

AKK

AKS

Oryctolagus

cuniculus

Hom

osapiens

AID

ELISA

003

0034

003

0037

003

148481

YTRD

LVYK

Rattu

snorvegicu

sHom

osapiens

AID

WI

148483

YVPIVT

FYSE

ISM

HSSRA

IPOryctolagus

cuniculus

MD

AID

ELISA

00002minus0007

0minus0002

150850

GY

Mus

musculus

Hum

anpapillomaviru

sAID

ELISA

150853

HIG

GLSI

LDPIFG

VL

Hom

osapiens

Dermatophagoides

farin

aeOOA

ACAb

B004

0038

004

0043

0039

107366

FPPK

PKD

Hom

osapiens

Hom

osapiens

OAID

ELISA

107376

GDRS

GYS

SPGSP

GMus

musculus

Hom

osapiens

AID

ACAb

Bminus003minus0028minus003minus0023minus0031

114859

ICGTD

GVTY

THom

osapiens

Gallusgallus

OOA

WI

114865

IVER

ETR

GQSE

NPL

WHALR

RRa

ttus

norvegicu

sHum

anherpesvirus

AID

ELISA

004

004

20035

0037

0038

62149

SVHLF

Hom

osapiens

MD

OAID

WI

62150

SVIALG

SQEG

ALH

QALA

GAI

Equu

scaballus

WestN

ilevirus

AID

IFAIHminus002minus0021minus0012minus0013minus0021

98455

SGSV

QFV

PIQ

Mus

musculus

Neisseria

meningitid

isAID

ELISA

98456

SICS

NNP

TCWAICK

RIPN

KKMus

musculus

Hum

anrespira

tory

virus

AID

IFAIH

004

004

0027

004

004

1

61783

STNKA

VVS

LSBo

staurus

Bovine

respira

tory

AID

ELISA

61791

STNPK

PQRK

TKRN

TNRR

PQHom

osapiens

HepatitisC

virus

EWEIR

ACAb

B004

0035

0029

0037

0039

100318

NAPK

TFQ

FIN

Mus

musculus

Bacillusa

nthracis

AID

ELISA

100319

NASSEL

HLL

GFG

INAEN

NHR

Hom

osapiens

Arachish

ypogaea

EWED

ELISA

0minus0002minus0012

00

10 Journal of Immunology ResearchTa

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

118947

PFSA

PPPA

Hom

osapiens

Hom

osapiens

OAID

ELISA

118948

PGAIEQG

PADDPG

EGPS

TGP

Hom

osapiens

Hum

anherpesvirus

NI

ACAb

Bminus004minus004minus004

1minus0036minus004

1

78323

YSFR

DMus

musculus

Bluetongue

virus1

AID

ELISA

78341

AALT

AEN

TAIK

KRN

ADAKA

Hom

osapiens

Streptococcus

mutan

sEE

EEL

ISA

001

0008

0007

0013

001

145831

IPLG

TRP

Mus

musculus

Hum

anpapillomaviru

sAID

ELISA

145841

KEDFR

YAISST

NEI

GLL

GA

Susscrofa

Classicalsw

ine

AID

PACminus004minus004minus0045minus004minus0043

119592

HTF

PAVLQ

Hom

osapiens

Hom

osapiens

OAID

ELISA

119596

IHIPSE

KIWRP

DLV

LYMus

musculus

Hom

osapiens

AID

RIA

001

0012

001

0017

000

9

58780

SKAANLSIIK

MD

Beetnecrotic

MD

WI

58783

SKAFSN

CYPY

DVP

DYA

SLOryctolagus

cuniculus

Influ

enza

Avirus

AID

RIAminus001minus000

4minus0014minus000

9minus0013

115153

MKG

VVC

TRIYEK

VHom

osapiens

Hom

osapiens

NI

ELISA

115155

NNQRK

KAKN

TPFN

MLK

RERN

Mus

musculus

Denguev

irus2

AID

ELISA

001

0012

000

40013

001

133629

LPLR

FOryctolagus

cuniculus

Gallusgallus

AID

ACAb

B133630

LPPG

LHV

FPLA

SNRS

Mus

musculus

MD

AID

SPRminus001minus0013minus0021minus001minus001

96215

EEEE

AE

DKE

DHom

osapiens

Hom

osapiens

OAID

ELISA

96216

EEEG

LLK

KSADTL

WNMQK

Mus

musculus

Mus

musculus

AID

ELISA

008

0082

0078

0087

008

39782

LTAASV

Hom

osapiens

Triticum

aestivum

OOA

ELISA

39788

LTAEL

KIYS

VIQ

AEI

NKH

LOryctolagus

cuniculus

Yersiniapestis

AID

ACAb

B001

0014minus0001

0007

000

9

107479

KFNWYV

DHom

osapiens

Hom

osapiens

OAID

ELISA

107482

KGEP

GL

PGRG

FPGFP

Mus

musculus

Hom

osapiens

AID

ACAb

B0

0002

00007minus0001

63569

TETV

NSD

IMacaca

mulatta

Shigellaflexneri

AID

ELISA

63573

TEVEL

KER

KHRIED

AVRN

AK

Hom

osapiens

Mycobacteriu

mlep

rae

OOID

ELISA

005

0036

004

10049

005

134028

DDTIS

Mus

musculus

Hom

osapiens

AID

NIAA

134029

DED

ENQS

PRSFQKK

TROryctolagus

cuniculus

Hom

osapiens

AID

ELISA

005

0053

005

005

004

8

23028

GVKY

AHom

osapiens

MD

OAID

WI

23032

GVLA

KDV

RFSQ

VHom

osapiens

MD

EWEIR

ACAb

B0

00

000

4minus0003

115293

IMCV

KKILDK

Hom

osapiens

Bostaurus

DEW

EDWI

115295

INPS

KEN

LCST

FCK

EVVRN

AHom

osapiens

Bostaurus

OOA

FIAminus003minus003minus003minus002minus0029

65105

TLTP

ENTL

Mus

musculus

Shigellaflexneri

AID

ELISA

65110

TLTS

GSD

LDRC

TTFD

DV

Oryctolagus

cuniculus

SARS

coronavirus

AID

ELISA

001

0013minus0003

001

001

1344

71PK

PEQ

Mus

musculus

Streptococcus

pneumoniae

AID

FACS

1344

72PL

LPGT

STTS

TGPC

KTHom

osapiens

HepatitisB

virus

AID

ELISA

002

0018

0019

002

0016

Journal of Immunology Research 11Ta

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

142228

LYCY

EQLN

DSSE

Hom

osapiens

Hum

anpapillomaviru

sNI

ELISA

142250

NWGDEP

SKRR

DRS

NSR

GRK

NFelis

catus

Felin

einfectio

usAID

ELISA

007

006

80071

0073

007

21549

GNYD

FWYQ

SHom

osapiens

Staphylococcus

aureus

OOID

ELISA

21553

GNYN

YKY

RYLR

HGK

LRPF

ERMus

musculus

SARS

coronavirus

AID

ELISA

003

0032

0021

0031

003

21549

GNYD

FWYQ

SHom

osapiens

Staphylococcus

aureus

OOID

ELISA

21553

GNYN

YKY

RYLR

HGK

LRPF

EROryctolagus

cuniculus

SARS

coronavirus

AID

ELISA

003

0034

0021

0031

003

34908

LAPL

GE

Hom

osapiens

HepatitisE

virus

EWEIR

ELISA

34914

LAPS

TLRS

LRKR

RLSSP

Hom

osapiens

Hum

anherpesvirus

OOID

ELISA

006

006

0059

0063

006

130454

CLFP

NNSY

CMus

musculus

MD

AID

NIAA

130456

CRPQ

VN

NPK

EWS

CAAC

Hom

osapiens

MD

OOD

ACAb

B001

0008

001

0011

0007

1964

4GFV

PSM

Hom

osapiens

Hepatitisd

elta

virus

OOID

ELISA

1964

7GFV

SASI

FGFQ

AEV

GPN

NTR

Oryctolagus

cuniculus

VacciniavirusW

RAID

ELISAminus001minus000

6minus0013minus000

9minus001

20678

GKRP

EMus

musculus

Streptococcus

pyogenes

AID

WI

20680

GKS

KRD

AKN

NAAK

LAVDKL

LMus

musculus

VacciniavirusW

RAID

WI

001

001

0001

001

001

123278

GYL

KDLP

TTOvisa

ries

Fasciolahepatica

AID

ELISA

123282

HAC

QKK

LLKF

EAL

QQEE

GEE

Rattu

snorvegicu

sGallusgallus

AID

PFFminus002minus0016minus0016minus002minus0025

141067

PLSLEP

DP

Mus

musculus

Hom

osapiens

AID

FACS

141073

REGVRW

RVMAIQ

Mus

musculus

Hom

osapiens

AID

ELISA

006

006

006

006

0056

139248

SFAG

TVIE

Mus

musculus

Classicalsw

ine

AID

WI

139305

TAAQ

ITQ

RKWEA

ARE

AEQ

RROryctolagus

cuniculus

Hom

osapiens

AID

IP003

0033

0026

003

003

104515

HDCR

PKKI

Mus

musculus

LaCrossevirus

AID

IFAIH

104520

IAKE

QE

NKE

TIGT

LKKI

LDE

Rattu

snorvegicu

sStreptococcus

pyogenes

AID

ELISAminus006minus006minus0073minus006minus0061

113517

HLY

ADGLT

DMus

musculus

Hum

anpapillomaviru

sAID

IFAIH

113518

HNKI

QA

IELE

DLL

RYS

KLYR

Mus

musculus

Hom

osapiens

AID

ELISA

002

002

0011

002

0019

156970

VER

HQ

Hom

osapiens

Hom

osapiens

OAID

ELISA

156975

WSSTV

LRVS

PTRT

VP

Mus

musculus

MD

AID

ELISA

001

0012

0003

0017

001

78252

PVQNLT

Mus

musculus

Porphyromonas

gingivalis

AID

WI

78253

QGGCG

RGWAFS

ATG

AIEA

Mus

musculus

Glycinem

axAID

ELISA

002

002

0017

002

0018

6068

CCPD

KNKS

Mus

musculus

Hum

anherpesvirus

AID

WI

6074

CCRH

KQKD

VGDVK

QTL

PPS

Ovisa

ries

MD

AID

ELISA

001

000

6000

4001

0008

12 Journal of Immunology Research

Table3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

53109

RAGVC

YHom

osapiens

Triticum

aestivum

OOA

ELISA

53116

RAILTA

FSPA

QDI

WGTS

Oryctolagus

cuniculus

SARS

coronavirus

AID

ELISAminus002minus0016minus0037minus0023minus002

147041

IPEQ

Hom

osapiens

Triticum

aestivum

OOA

WI

147064

KHQGA

QYV

WN

RTA

Hom

osapiens

Bostaurus

OOID

WI

006

006

0047

0057

006

7066

4VQ

VVYD

YQOryctolagus

cuniculus

Treponem

apallidu

mAID

ELISA

70667

VQWMN

RLIAFA

FAG

NHVS

PHom

osapiens

HepatitisC

virus

OOID

ELISA

005

004

6004

10049

005

134343

DLY

IKMus

musculus

Hum

anpapillomaviru

sAID

PhDIP

134344

DMAQ

VTV

GPG

LLGVS

TLMus

musculus

Hom

osapiens

AID

PhDIP

00minus000

90

0

Journal of Immunology Research 13

The first input term is the value 120582(120576119894119895)ref is the scoring

function 120582 of the efficiency of the initial process 120576119894119895(known

solution) The function 120582(120576119894119895)ref = 1 if the 119894th peptide could

experimentally be demonstrated to be a B-epitope in theassay of reference (reference) carried out in the conditions119888119895 120582(120576119894119895)ref = 0 otherwise The variational-perturbation

terms ΔΔ120594119888119895are at the same time terms typical of pertur-

bation theory and moving average (MA) functions used inBox-Jenkin models in time series [60] These new types ofterms account both for the deviation of the electronegativityof all amino acids in the sequence of the new peptidewith respect to the peptide of reference and with respectto all boundary conditions In Table 1 we give the overallclassification results obtainedwith thismodel Speck-Plancheet al [61ndash63] introduced different multitargetmultiplexingQSARmodels that incorporate this type of information basedon MAs The results obtained with the present model areexcellent comparedwith other similarmodels in the literatureuseful for other problems including moving average models[64 65] or perturbation models [58] Notably this is also thefirst model combining both perturbation theory and MAs ina QSPR context

The other input terms are the following The first Δ120594seq =120594(119898119902)ref minus120594(119898119894)new is the perturbation term for the variation

or in themean value of electronegativity for all amino acids inthe sequence of the peptide of reference The remnant inputvariables of the model ΔΔ120594

119888119895= Δ120594

119888119895-ref minus Δ120594119888119895-new =

[120594(119898119902)ref minus

lowast120594 (119888119902119903)ref] minus [120594(119898119894)new minus

lowast120594 (119888119894119895)new] quantify

values of the conditions of the new assay 119888119895-new that representperturbations with respect to the initial conditions 119888

119894119895-ref of

the assay of reference The quantities lowast120594(119888119894119895) and lowast120594(119888

119902119903) are

the average values of the mean electronegativity values 120594(119898119894)

and 120594(119898119902) for all new and reference peptides in IEDB that

are epitopes under the 119895th or 119903th boundary condition Thevalues of these terms have been tabulated for gt500 sourceorganisms gt50 host organisms gt10 biological process andgt30 experimental techniques We must substitute the valuesof 120594(119898

119894) and 120594(119898

119902) of the new and reference peptides and the

tabulated values of lowast120594(119888119894119895) and lowast120594(119888

119902119903) for all combinations

of boundary conditions to predict the perturbations of theaction as epitope of peptides In doing so we can found theoptimal sequence and boundary conditions towards the useof the peptide in the development of a vaccine In Table 2 wegive some of these values of lowast120594(119888

119894119895) and lowast120594(119888

119902119903)

In Table 3 we depict the sequences and input-outputboundary conditions for top perturbations present in IEDBAll these perturbations have observed value of 120582(120576

119894119895)new = 1

and predicted value also equal to 1 with a high probabilitySee Supplementary Material available online at httpdxdoiorg1011552014768515 file contains a full list of gt200000cases of perturbations

4 Conclusions

It is possible to develop generalmodels for vaccine design ableto predict the results of multiple input-output perturbations

in peptide sequence and experimental assay boundary condi-tions using ideas of QSPR analysis perturbation theory andBox and Jenkins MA operators The electronegativity valuescalculated withMARCH-INSIDE seem to be goodmoleculardescriptors for this type of QSPR-perturbation models

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

The present study was partially supported by GrantsAGL2010-22290-C02 and AGL2011-30563-C03 from Minis-terio de Ciencia e Innovacion Spain and Grant CN 2012155from Xunta de Galicia Spain

References

[1] B Peters J Sidney P Bourne et al ldquoThe design and implemen-tation of the immune epitope database and analysis resourcerdquoImmunogenetics vol 57 no 5 pp 326ndash336 2005

[2] B Peters J Sidney P Bourne et al ldquoThe immune epitopedatabase and analysis resource from vision to blueprintrdquo PLoSBiology vol 3 no 3 p e91 2005

[3] PWang A AMorgan Q Zhang A Sette and B Peters ldquoAuto-mating document classification for the Immune Epitope Data-baserdquo BMC Bioinformatics vol 8 article 269 2007

[4] A Sette ldquoThe immune epitope database and analysis resourcefrom vision to blueprintrdquo Genome Informatics vol 15 no 2 p299 2004

[5] N Salimi W Fleri B Peters and A Sette ldquoThe immune epi-tope database a historical retrospective of the first decaderdquoImmunology vol 137 no 2 pp 117ndash123 2012

[6] Y Kim A Sette and B Peters ldquoApplications for T-cell epitopequeries and tools in the Immune EpitopeDatabase andAnalysisResourcerdquo Journal of Immunological Methods vol 374 no 1-2pp 62ndash69 2011

[7] Y Kim J Ponomarenko Z Zhu D Tamang et al ldquoImmuneepitope database analysis resourcerdquo Nucleic Acids Research ppW525ndashW530 2012

[8] A M Helguera R D Combes M P Gonzalez and M N DS Cordeiro ldquoApplications of 2D descriptors in drug design aDRAGON talerdquo Current Topics in Medicinal Chemistry vol 8no 18 pp 1628ndash1655 2008

[9] GM Casanola-Martın Y Marrero-Ponce M T H Khan et alldquoDragon method for finding novel tyrosinase inhibitors Biosil-ico identification and experimental in vitro assaysrdquo The Euro-pean Journal of Medicinal Chemistry vol 42 no 11-12 pp 1370ndash1381 2007

[10] I V Tetko J Gasteiger R Todeschini et al ldquoVirtual computa-tional chemistry laboratorymdashdesign and descriptionrdquo Journalof Computer-AidedMolecular Design vol 19 no 6 pp 453ndash4632005

[11] A R Katritzky A Oliferenko A Lomaka and M KarelsonldquoSix-membered cyclic ureas as HIV-1 protease inhibitors aQSAR study based on CODESSA PRO approachrdquo Bioorganicand Medicinal Chemistry Letters vol 12 no 23 pp 3453ndash34572002

14 Journal of Immunology Research

[12] A R Katritzky S Perumal R Petrukhin and E Klein-peter ldquoCODESSA-based theoretical QSPRmodel for hydantoinHPLC-RT lipophilicitiesrdquo Journal of Chemical Information andComputer Sciences vol 41 no 3 pp 569ndash574 2001

[13] S Vilar G Cozza and S Moro ldquoMedicinal chemistry and themolecular operating environment (MOE) application of QSARand molecular docking to drug discoveryrdquo Current Topics inMedicinal Chemistry vol 8 no 18 pp 1555ndash1572 2008

[14] G Marzaro A Chilin A Guiotto et al ldquoUsing the TOPS-MODE approach to fit multi-target QSAR models for tyrosinekinases inhibitorsrdquo The European Journal of Medicinal Chem-istry vol 46 no 6 pp 2185ndash2192 2011

[15] S Vilar E Estrada E Uriarte L Santana and Y Gutierrez ldquoInsilico studies toward the discovery of new anti-HIV nucleosidecompounds through the use of tops-mode and 2D3D con-nectivity indices 2 Purine derivativesrdquo Journal of ChemicalInformation and Modeling vol 45 no 2 pp 502ndash514 2005

[16] E Estrada J A Quincoces and G Patlewicz ldquoCreating molec-ular diversity from antioxidants in Brazilian propolis combina-tion of TOPS-MODE QSAR and virtual structure generationrdquoMolecular Diversity vol 8 no 1 pp 21ndash33 2004

[17] E Estrada andHGonzalez ldquoWhat are the limits of applicabilityfor graph theoretic descriptors in QSPRQSAR modelingdipole moments of aromatic compounds with TOPS-MODEdescriptorsrdquo Journal of Chemical Information and ComputerSciences vol 43 no 1 pp 75ndash84 2003

[18] Y Marrero-Ponce J A Castillo-Garit E Olazabal et al ldquoTom-ocomd-Cardd a novel approach for computer-aided ldquorationalrdquodrug design I Theoretical and experimental assessment of apromising method for computational screening and in silicodesign of new anthelmintic compoundsrdquo Journal of Computer-Aided Molecular Design vol 18 no 10 pp 615ndash634 2004

[19] Y Marrero Ponce R Medina Marrero E A Castro et al ldquoPro-tein quadratic indices of the ldquomacromolecular pseudographrsquos 120572-carbon atom adjacency matrixrdquo 1 prediction of arc repressoralanine-mutantrsquos stabilityrdquoMolecules vol 9 no 12 pp 1124ndash11472004

[20] H Gonzalez-Dıaz F Prado-Prado and F M Ubeira ldquoPredict-ing antimicrobial drugs and targets with the MARCH-INSIDEapproachrdquo Current Topics in Medicinal Chemistry vol 8 no 18pp 1676ndash1690 2008

[21] R Garcıa-Domenech J Galvez J V de Julian-Ortiz and LPogliani ldquoSomenew trends in chemical graph theoryrdquoChemicalReviews vol 108 no 3 pp 1127ndash1169 2008

[22] E Estrada and E Uriarte ldquoRecent advances on the role of topo-logical indices in drug discovery researchrdquo Current MedicinalChemistry vol 8 no 13 pp 1573ndash1588 2001

[23] R Todeschini andVConsonniHandbook ofMolecularDescrip-tors Wiley-VCH Weinheim Germany 2008

[24] E Estrada E J Delgado J B Alderete and G A Jana ldquoQuan-tum-connectivity descriptors inmodeling solubility of environ-mentally important organic compoundsrdquo Journal of Computa-tional Chemistry vol 25 no 14 pp 1787ndash1796 2004

[25] E Besalu X Girones L Amat andR Carbo-Dorca ldquoMolecularquantum similarity and the fundamentals of QSARrdquo Accountsof Chemical Research vol 35 no 5 pp 289ndash295 2002

[26] D A Rincon M N D S Cordeiro and R A Mosquera ldquoOnthe electronic structure of cocaine and its metabolitesrdquo Journalof Physical Chemistry A vol 113 no 50 pp 13937ndash13942 2009

[27] M Mandado M J Gonzalez-Moa and R A MosqueraldquoChemical graph theory and n-center electron delocalization

indices a study on polycyclic aromatic hydrocarbonsrdquo Journalof Computational Chemistry vol 28 no 10 pp 1625ndash1633 2007

[28] THill andP Lewicki STATISTICSMethods andApplications AComprehensive Reference for Science Industry and DataMiningStatSoft Tulsa Okla USA 2006

[29] E Frank M Hall L Trigg G Holmes and I H Witten ldquoDatamining in bioinformatics using Wekardquo Bioinformatics vol 20no 15 pp 2479ndash2481 2004

[30] G Patlewicz N Ball E D Booth E Hulzebos E Zvinavasheand C Hennes ldquoUse of category approaches read-across and(Q)SAR general considerationsrdquo Regulatory Toxicology andPharmacology vol 67 no 1 pp 1ndash12 2013

[31] D W Roberts and G Y Patlewicz ldquoUpdating the skin sensiti-zation in vitro data assessment paradigm in 2009mdasha chemistryand QSAR perspectiverdquo Journal of Applied Toxicology vol 30no 3 pp 286ndash288 2010

[32] D W Roberts and G Y Patlewicz ldquoNonanimal alternatives forskin sensitization letter to the editorrdquoToxicological Sciences vol106 no 2 pp 572ndash574 2008

[33] E Estrada G Patlewicz and Y Gutierrez ldquoFrom knowledgegeneration to knowledge archive a general strategy usingTOPS-MODE with DEREK to formulate new alerts for skinsensitizationrdquo Journal of Chemical Information and ComputerSciences vol 44 no 2 pp 688ndash698 2004

[34] G F Gerberick C A Ryan P S Kern et al ldquoA chemical datasetfor evaluation of alternative approaches to skin-sensitizationtestingrdquo Contact Dermatitis vol 50 no 5 pp 274ndash288 2004

[35] G Y Patlewicz D A Basketter C K Smith Pease et al ldquoFurtherevaluation of quantitative structurendashactivity relationship mod-els for the prediction of the skin sensitization potency ofselected fragrance allergensrdquo Contact Dermatitis vol 50 no 2pp 91ndash97 2004

[36] E Estrada G Patlewicz M Chamberlain D Basketter andS Larbey ldquoComputer-aided knowledge generation for under-standing skin sensitization mechanisms the TOPS-MODEapproachrdquo Chemical Research in Toxicology vol 16 no 10 pp1226ndash1235 2003

[37] G Patlewicz R Rodford and J D Walker ldquoQuantitative struc-ture-activity relationships for predicting skin and eye irritationrdquoEnvironmental Toxicology and Chemistry vol 22 no 8 pp1862ndash1869 2003

[38] G Y Patlewicz R A Rodford G Ellis and M D Barratt ldquoAQSAR model for the eye irritation of cationic surfactantsrdquo Tox-icology In Vitro vol 14 no 1 pp 79ndash84 2000

[39] E Tenorio-Borroto C G Penuelas Rivas J C VasquezChagoyan N Castanedo F J Prado-Prado and X Garcia-Mera ldquoANN multiplexing model of drugs effect on macro-phages theoretical and flow cytometry study on the cyto-toxicity of the anti-microbial drug G1 in spleenrdquo Bioorganic ampMedicinal Chemistry vol 20 no 20 pp 6181ndash6194 2012

[40] D R Flower H McSparron M J Blythe et al ldquoComputationalvaccinology quantitative approachesrdquo Novartis FoundationSymposium vol 254 pp 102ndash120 2003

[41] I A Doytchinova P Guan and D R Flower ldquoQuantitativestructure-activity relationships and the prediction of MHCsupermotifsrdquoMethods vol 34 no 4 pp 444ndash453 2004

[42] Y Xiao and M R Segal ldquoPrediction of genomewide conservedepitope profiles of HIV-1 classifier choice and peptide rep-resentationrdquo Statistical Applications in Genetics and MolecularBiology vol 4 no 1 article 25 2005

Journal of Immunology Research 15

[43] T Fagerberg V Zoete S Viatte P Baumgaertner P M Alves PRomero et al ldquoPrediction of cross-recognition of peptide-HLAA2 by melan-a-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationshipsrdquo PLoSONE vol 8 no 7 Article ID e65590 2013

[44] D Barh A N Misra A Kumar and A Vasco ldquoA novel strategyof epitope design inNeisseria gonorrhoeaerdquo Bioinformation vol5 no 2 pp 77ndash82 2010

[45] J Bi R Song H Yang et al ldquoStepwise identification of HLA-Alowast0201-restricted CD8+ T-cell epitope peptides from herpessimplex virus type 1 genome boosted by a steprank schemerdquoBiopolymers vol 96 no 3 pp 328ndash339 2011

[46] R D Bremel and E J Homan ldquoAn integrated approach to epit-ope analysis II a system for proteomic-scale prediction ofimmunological characteristicsrdquo Immunome Research vol 6 no1 article 8 2010

[47] C M Diez-Rivero B Chenlo P Zuluaga and P A RecheldquoQuantitative modeling of peptide binding to TAP using sup-port vector machinerdquo Proteins vol 78 no 1 pp 63ndash72 2010

[48] E Martınez-Naves E M Lafuente and P A Reche ldquoRecogni-tion of the ligand-type specificity of classical and non-classicalMHC I proteinsrdquo FEBS Letters vol 585 no 21 pp 3478ndash34842011

[49] M Bhasin E L Reinherz and P A Reche ldquoRecognition andclassification of histones using support vectormachinerdquo Journalof Computational Biology vol 13 no 1 pp 102ndash112 2006

[50] P A Reche and E L Reinherz ldquoPEPVAC a web server formulti-epitope vaccine development based on the prediction ofsupertypic MHC ligandsrdquo Nucleic Acids Research vol 33 no 2pp W138ndashW142 2005

[51] E M Lafuente and P A Reche ldquoPrediction of MHC-peptidebinding a systematic and comprehensive overviewrdquo CurrentPharmaceutical Design vol 15 no 28 pp 3209ndash3220 2009

[52] A Gaulton L J Bellis A P Bento et al et al ldquoA large-scalebioactivity database for drug discoveryrdquoNucleic Acids Researchpp D1100ndashD1107 2012

[53] W H Cropper Great Physicists The Life and Times of LeadingPhysicists from Galileo to Hawking Oxford University PressNew York NY USA 2004

[54] E L Bouzarth A Brooks R Camassa et al ldquoEpicyclic orbits ina viscous fluid about a precessing rod theory and experimentsat the micro-and macro-scalesrdquo Physical Review E vol 76 no1 part 2 Article ID 016313 2007

[55] M Laberge ldquoIntrinsic protein electric fields basic non-covalentinteractions and relationship to protein-induced Stark effectsrdquoBiochimica et BiophysicaActa vol 1386 no 2 pp 305ndash330 1998

[56] FM Fernandez and J AMorales ldquoPerturbation theorywithoutwave functions for the Zeeman effect in hydrogenrdquo PhysicalReview A vol 46 no 1 pp 318ndash326 1992

[57] B D Marshall and W G Chapman ldquoThermodynamic pertur-bation theory for associating fluids with small bond angleseffects of steric hindrance ring formation and double bondingrdquoPhysical Reiew E vol 87 no 5 Article ID 052307 12 pages 2013

[58] H Gonzalez-Diaz S Arrasate A Gomez-San Juan et al ldquoNewtheory for multiple input-output perturbations in complexmolecular systems 1 Linear QSPR electronegativity models inphysical organic and medicinal chemistryrdquo Current Topics inMedicinal Chemistry 2013

[59] H Gonzalez-Diaz S Arrasate N Sotomayor et al ldquoMIANNmodels in medicinal physical and organic chemistryrdquo CurrentTopics in Medicinal Chemistry vol 13 no 5 pp 619ndash641 2013

[60] G E P Box and GM Jenkins Time Series Analysis Forecastingand Control Holden-Day San Francisco Calif USA 1970

[61] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoUnified multi-target approach for the rational insilico design of anti-bladder cancer agentsrdquo Anti-Cancer Agentsin Medicial Chemistry vol 13 no 5 pp 791ndash800 2013

[62] A Speck-Planche V V Kleandrova and M N Cordeiro ldquoNewinsights toward the discovery of antibacterial agents multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugsrdquo TheEuropean Journal of Pharmaceutical Sciences vol 48 no 4-5pp 812ndash818 2013

[63] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoMulti-target inhibitors for proteins associated withAlzheimer in silico discovery using fragment-based descrip-torsrdquo Current Alzheimer Research vol 10 no 2 pp 117ndash1242013

[64] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoIn silico discovery and virtual screening of multi-target inhibitors for proteins in Mycobacterium tuberculosisrdquoCombinatorial Chemistry amp High Throughput Screening vol 15no 8 pp 666ndash673 2012

[65] A Speck-Planche V V Kleandrova F Luan and M N DS Cordeiro ldquoChemoinformatics in anti-cancer chemotherapymulti-target QSAR model for the in silico discovery of anti-breast cancer agentsrdquo The European Journal of PharmaceuticalSciences vol 47 no 1 pp 273ndash279 2012

Submit your manuscripts athttpwwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MEDIATORSINFLAMMATION

of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Behavioural Neurology

EndocrinologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Disease Markers

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

OncologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Oxidative Medicine and Cellular Longevity

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational and Mathematical Methods in Medicine

OphthalmologyJournal of

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Diabetes ResearchJournal of

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Research and TreatmentAIDS

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Gastroenterology Research and Practice

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Page 7: Research Article Model for Vaccine Design by …downloads.hindawi.com/journals/jir/2014/768515.pdfResearch Article Model for Vaccine Design by Prediction of B-Epitopes of IEDB Given

Journal of Immunology Research 7Ta

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

23028

GVKY

AHom

osapiens

MD

OAID

WI

23032

GVLA

KDVRF

SQV

Hom

osapiens

MD

OOID

ELISA

00

00007minus0002

51199

QKK

AIE

Oryctolagus

cuniculus

Vibriocholerae

AID

ELISA

51204

QKK

NK

RNTN

RRPQ

DV

Hom

osapiens

HepatitisC

virus

OOID

ELISA

003

0026

0018

0029

003

144783

SHVVT

MLD

NF

Hom

osapiens

Hom

osapiens

NI

ELISA

144786

SMNRG

RGTH

PSLIWM

Mus

musculus

MD

AID

ACAb

B003

0032

0023

0033

0029

134343

DLY

IKMus

musculus

Hum

anpapillomaviru

sAID

NIAA

134344

DMAQ

VTV

GPG

LLGVS

TLMus

musculus

Hom

osapiens

AID

WI

00minus000

90

0

38321

LNQLA

GRM

Anas

platyrhynchos

Duckhepatitis

AID

ELISA

38323

LNQTA

RAFP

DCA

ICW

EPSP

POryctolagus

cuniculus

Bovine

leukemia

virus

AID

ACAb

Bminus001minus0008minus0022minus001minus0011

144657

GQITVD

MMYG

Hom

osapiens

Hom

osapiens

OAID

ELISA

1446

61GRE

GYP

ADGGCA

WPA

CYC

Oryctolagus

cuniculus

MD

AID

WI

002

0024

0013

0027

0022

21084

GLQ

NMus

musculus

Chlamydia

trachom

atis

AID

ELISA

21093

GLR

AQD

DFS

GWDI

NTP

AFE

WMus

musculus

Mycobacteriu

mtuberculosis

AID

WI

003

003

0013

003

0032

98453

SGFS

GSV

QFV

Oryctolagus

cuniculus

Neisseria

meningitid

isAID

ELISA

98456

SICS

NN

PTCW

AIC

KRIPNKK

Mus

musculus

Hum

anrespira

tory

virus

AID

IFAIH

004

0037

0027

004

004

1

98453

SGFS

GSV

QFV

Mus

musculus

Neisseria

meningitid

isAID

ELISA

98456

SICS

NN

PTCW

AIC

KRIPNKK

Mus

musculus

Hum

anrespira

tory

virus

AID

IFAIH

004

004

0027

004

004

1

107107

EAIQ

PRa

ttus

norvegicu

sHom

osapiens

AID

ELISA

107110

EKER

RPSP

IGTA

TLL

Hom

osapiens

MD

OOA

ELISA

005

004

80043

0053

005

110857

FTGEA

YSY

WSA

KHom

osapiens

Mycoplasm

apenetra

nsEW

EIR

ELISA

110859

GEE

SRIS

LPLP

NF

SSLN

LRE

Mus

musculus

Hom

osapiens

AID

FIA

00002minus0017

0003

0003

36315

LGSA

YPMus

musculus

Mycobacteriu

mlep

rae

MD

ELISA

36317

LGSG

AFG

TIYK

GMus

musculus

Avian

erythroblasto

sisvirus

AID

ACAb

B001

001

00011

000

9

122034

WNPA

DRa

ttus

norvegicu

sTorpedocalifornica

AID

ELISA

122035

WNPA

DYG

GIK

WN

PADYG

GIK

Rattu

snorvegicu

sMD

AID

RIA

001

001

0001

001

000

9

25013

HVA

DID

KLID

Mus

musculus

Puum

alavirus

Kazan

AID

ELISA

25021

HVA

PTH

YVTE

SDA

SQRV

TQL

Hom

osapiens

HepatitisC

virus

OOID

ELISA

0minus0002minus0014minus0001

0

36162

LGIH

EOryctolagus

cuniculus

Cand

idaalbicans

AID

ELISA

36166

LGIM

GE

YRGTP

RNQDLY

DAA

Mus

musculus

Hum

anrespira

tory

virus

AID

RIA

0minus0003minus0007

0minus0001

67253

TWEV

LHMus

musculus

Plasmodium

vivax

AID

ELISA

67257

TWGEN

ETDVLL

LNNTR

PPQ

Hom

osapiens

HepatitisC

virus

OOID

ACAb

Bminus002minus0022minus0027minus0021minus0021

8 Journal of Immunology Research

Table3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

50990

QGYR

VSSY

LPHom

osapiens

Hevea

brasiliensis

OOA

WI

50998

QHEQ

DR

PTPS

PAP

SRPF

SVL

Hom

osapiens

HepatitisE

virus

OOID

ELISA

001

001minus0002

0007

0008

100458

RDVLQ

LYAPE

Mus

musculus

Bacillusa

nthracis

AID

ELISA

1004

62RF

STRY

GNQNGRI

RVLQ

RFD

Hom

osapiens

Arachish

ypogaea

EWED

ELISA

003

0028

0018

003

003

111036

TEST

FTGEA

YSV

Hom

osapiens

Mycoplasm

apenetra

nsEW

EIR

ELISA

111039

TGVPID

PAVPD

SSIV

PLLE

SBo

staurus

Bovine

papillomaviru

sAID

ELISA

003

0035

002

0033

003

117919

IFIEME

Hom

osapiens

Hom

osapiens

OAID

WI

117921

IGIIDLIE

KRKF

NQ

Mus

musculus

Hom

osapiens

AID

WI

003

0032

003

0037

003

7127

CTDTD

KLF

Oryctolagus

cuniculus

Shigellaflexneri

AID

ELISA

7128

CTDVS

TAIH

ADQL

TPAW

Hom

osapiens

SARS

coronavirus

OOID

ELISA

001

000

6minus0003

000

9001

112253

PGQSP

KLHom

osapiens

Hom

osapiens

OAID

ELISA

112255

PIRA

LVGDEV

ELP

CRISPG

KMus

musculus

Hom

osapiens

AID

ELISA

001

0012

001

0017

001

122034

WNPA

DRa

ttus

norvegicu

sTorpedocalifornica

AID

ELISA

122038

WNPD

DY

GGVKW

NP

DDYG

GVK

Rattu

snorvegicu

sMD

AID

RIA

00minus000

90minus0001

131878

FLMLV

GGST

LHom

osapiens

Hom

osapiens

OAID

ACAb

B131879

FLVA

HT

RARA

PSA

GER

ARR

SMus

musculus

Mus

musculus

AID

NIAA

003

0032

0028

0037

0033

7066

4VQ

VVYD

YQHom

osapiens

Treponem

apallidu

mOOID

ELISA

70667

VQWMNR

LIAFA

FAG

NHVS

PHom

osapiens

HepatitisC

virus

OOID

ELISA

005

005

004

1005

005

71545

VTV

Hom

osapiens

Helicobacterpylori

OOID

ELISA

71559

VTV

RGGL

RILSPD

RKHom

osapiens

Arachish

ypogaea

OOA

WI

004

004

0032

0043

004

2

127856

TDVRY

KD

Mus

musculus

Mus

musculus

AID

ACAb

B127857

TDVRY

KDDMYH

FFCP

AIQ

AQMus

musculus

Mus

musculus

AID

PFF

001

001

001

001

000

6

112149

GVG

WIRQ

Hom

osapiens

Hom

osapiens

OAID

ELISA

112152

HHPA

RTAHYG

SLP

QKS

HGRT

Hom

osapiens

Hom

osapiens

AID

ELISA

00

00007

0

119581

FSCS

VMHE

Hom

osapiens

Hom

osapiens

OAID

ELISA

119582

GLQ

LIQL

INVDEV

NQI

Mus

musculus

Hom

osapiens

AID

RIAminus001minus0008minus001minus0003minus0011

144657

GQITVD

MMYG

Hom

osapiens

Hom

osapiens

OAID

ELISA

144659

GRE

GYP

ADGGAA

GYC

NTE

Oryctolagus

cuniculus

MD

AID

WIminus001minus000

6minus0017minus0003minus0008

25013

HVA

DI

DKL

IDMus

musculus

Puum

alavirus

Kazan

AID

ELISA

25022

HVA

PTH

YVVES

DA

SQRV

TQV

Hom

osapiens

HepatitisC

virus

OOID

ELISA

0minus0002minus0014minus0001

0

Journal of Immunology Research 9Ta

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

144652

GMRG

MKG

LVY

Hom

osapiens

Hom

osapiens

OAID

ELISA

144654

GPH

PTLE

VVPM

GRG

SMus

musculus

MD

AID

ELISAminus002minus0018minus0027minus0013minus002

104515

HDCR

PKKI

Mus

musculus

LaCrossevirus

AID

IFAIH

104521

IGTL

KKIL

DET

VKD

KIAKE

QRa

ttus

norvegicu

sStreptococcus

pyogenes

AID

ELISAminus004minus004minus0053minus004minus004

1

7367

CYGDWA

Hom

osapiens

Triticum

aestivum

OOA

ELISA

7374

CYGLP

DS

EPTK

TNGK

Mus

musculus

Tityus

serrulatus

AID

WIminus002minus0018minus0043minus0023minus0018

7367

CYGDWA

Hom

osapiens

Triticum

aestivum

OOA

ELISA

7374

CYGLP

DS

EPTK

TNGK

Mus

musculus

Tityus

serrulatus

AID

WIminus002minus0018minus0043minus0023minus0018

144610

DFF

TYK

Mus

musculus

Porcine

transmissible

AID

WI

144611

DFN

GSF

DMNGTITA

Oryctolagus

cuniculus

Escherich

iacoli

AID

ELISAminus001minus0007minus0018minus001minus0012

112047

AST

RESG

Hom

osapiens

Hom

osapiens

OAID

ELISA

112048

ATAST

MDHARH

GF

LPRH

RDT

Hom

osapiens

Hom

osapiens

AID

ELISA

005

005

005

0057

005

36136

LGGVFT

Hom

osapiens

Denguev

irus2

OOID

ELISA

36137

LGGWKL

QSD

PRAY

AL

Hom

osapiens

Ambrosia

artemisiifolia

OOA

RIA

001

001

0007

0013

000

9

115256

FREL

KDLK

GY

Hom

osapiens

Bostaurus

DEW

EDWI

115261

GDLE

ILL

QKW

ENG

ECAQ

KKI

Hom

osapiens

Bostaurus

OOA

FIAminus001minus001minus001

0minus000

9

129024

KADQLY

KHom

osapiens

Hom

osapiens

OAID

ELISA

129026

KAKK

PAAAAG

AKK

AKS

Oryctolagus

cuniculus

Hom

osapiens

AID

ELISA

003

0034

003

0037

003

148481

YTRD

LVYK

Rattu

snorvegicu

sHom

osapiens

AID

WI

148483

YVPIVT

FYSE

ISM

HSSRA

IPOryctolagus

cuniculus

MD

AID

ELISA

00002minus0007

0minus0002

150850

GY

Mus

musculus

Hum

anpapillomaviru

sAID

ELISA

150853

HIG

GLSI

LDPIFG

VL

Hom

osapiens

Dermatophagoides

farin

aeOOA

ACAb

B004

0038

004

0043

0039

107366

FPPK

PKD

Hom

osapiens

Hom

osapiens

OAID

ELISA

107376

GDRS

GYS

SPGSP

GMus

musculus

Hom

osapiens

AID

ACAb

Bminus003minus0028minus003minus0023minus0031

114859

ICGTD

GVTY

THom

osapiens

Gallusgallus

OOA

WI

114865

IVER

ETR

GQSE

NPL

WHALR

RRa

ttus

norvegicu

sHum

anherpesvirus

AID

ELISA

004

004

20035

0037

0038

62149

SVHLF

Hom

osapiens

MD

OAID

WI

62150

SVIALG

SQEG

ALH

QALA

GAI

Equu

scaballus

WestN

ilevirus

AID

IFAIHminus002minus0021minus0012minus0013minus0021

98455

SGSV

QFV

PIQ

Mus

musculus

Neisseria

meningitid

isAID

ELISA

98456

SICS

NNP

TCWAICK

RIPN

KKMus

musculus

Hum

anrespira

tory

virus

AID

IFAIH

004

004

0027

004

004

1

61783

STNKA

VVS

LSBo

staurus

Bovine

respira

tory

AID

ELISA

61791

STNPK

PQRK

TKRN

TNRR

PQHom

osapiens

HepatitisC

virus

EWEIR

ACAb

B004

0035

0029

0037

0039

100318

NAPK

TFQ

FIN

Mus

musculus

Bacillusa

nthracis

AID

ELISA

100319

NASSEL

HLL

GFG

INAEN

NHR

Hom

osapiens

Arachish

ypogaea

EWED

ELISA

0minus0002minus0012

00

10 Journal of Immunology ResearchTa

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

118947

PFSA

PPPA

Hom

osapiens

Hom

osapiens

OAID

ELISA

118948

PGAIEQG

PADDPG

EGPS

TGP

Hom

osapiens

Hum

anherpesvirus

NI

ACAb

Bminus004minus004minus004

1minus0036minus004

1

78323

YSFR

DMus

musculus

Bluetongue

virus1

AID

ELISA

78341

AALT

AEN

TAIK

KRN

ADAKA

Hom

osapiens

Streptococcus

mutan

sEE

EEL

ISA

001

0008

0007

0013

001

145831

IPLG

TRP

Mus

musculus

Hum

anpapillomaviru

sAID

ELISA

145841

KEDFR

YAISST

NEI

GLL

GA

Susscrofa

Classicalsw

ine

AID

PACminus004minus004minus0045minus004minus0043

119592

HTF

PAVLQ

Hom

osapiens

Hom

osapiens

OAID

ELISA

119596

IHIPSE

KIWRP

DLV

LYMus

musculus

Hom

osapiens

AID

RIA

001

0012

001

0017

000

9

58780

SKAANLSIIK

MD

Beetnecrotic

MD

WI

58783

SKAFSN

CYPY

DVP

DYA

SLOryctolagus

cuniculus

Influ

enza

Avirus

AID

RIAminus001minus000

4minus0014minus000

9minus0013

115153

MKG

VVC

TRIYEK

VHom

osapiens

Hom

osapiens

NI

ELISA

115155

NNQRK

KAKN

TPFN

MLK

RERN

Mus

musculus

Denguev

irus2

AID

ELISA

001

0012

000

40013

001

133629

LPLR

FOryctolagus

cuniculus

Gallusgallus

AID

ACAb

B133630

LPPG

LHV

FPLA

SNRS

Mus

musculus

MD

AID

SPRminus001minus0013minus0021minus001minus001

96215

EEEE

AE

DKE

DHom

osapiens

Hom

osapiens

OAID

ELISA

96216

EEEG

LLK

KSADTL

WNMQK

Mus

musculus

Mus

musculus

AID

ELISA

008

0082

0078

0087

008

39782

LTAASV

Hom

osapiens

Triticum

aestivum

OOA

ELISA

39788

LTAEL

KIYS

VIQ

AEI

NKH

LOryctolagus

cuniculus

Yersiniapestis

AID

ACAb

B001

0014minus0001

0007

000

9

107479

KFNWYV

DHom

osapiens

Hom

osapiens

OAID

ELISA

107482

KGEP

GL

PGRG

FPGFP

Mus

musculus

Hom

osapiens

AID

ACAb

B0

0002

00007minus0001

63569

TETV

NSD

IMacaca

mulatta

Shigellaflexneri

AID

ELISA

63573

TEVEL

KER

KHRIED

AVRN

AK

Hom

osapiens

Mycobacteriu

mlep

rae

OOID

ELISA

005

0036

004

10049

005

134028

DDTIS

Mus

musculus

Hom

osapiens

AID

NIAA

134029

DED

ENQS

PRSFQKK

TROryctolagus

cuniculus

Hom

osapiens

AID

ELISA

005

0053

005

005

004

8

23028

GVKY

AHom

osapiens

MD

OAID

WI

23032

GVLA

KDV

RFSQ

VHom

osapiens

MD

EWEIR

ACAb

B0

00

000

4minus0003

115293

IMCV

KKILDK

Hom

osapiens

Bostaurus

DEW

EDWI

115295

INPS

KEN

LCST

FCK

EVVRN

AHom

osapiens

Bostaurus

OOA

FIAminus003minus003minus003minus002minus0029

65105

TLTP

ENTL

Mus

musculus

Shigellaflexneri

AID

ELISA

65110

TLTS

GSD

LDRC

TTFD

DV

Oryctolagus

cuniculus

SARS

coronavirus

AID

ELISA

001

0013minus0003

001

001

1344

71PK

PEQ

Mus

musculus

Streptococcus

pneumoniae

AID

FACS

1344

72PL

LPGT

STTS

TGPC

KTHom

osapiens

HepatitisB

virus

AID

ELISA

002

0018

0019

002

0016

Journal of Immunology Research 11Ta

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

142228

LYCY

EQLN

DSSE

Hom

osapiens

Hum

anpapillomaviru

sNI

ELISA

142250

NWGDEP

SKRR

DRS

NSR

GRK

NFelis

catus

Felin

einfectio

usAID

ELISA

007

006

80071

0073

007

21549

GNYD

FWYQ

SHom

osapiens

Staphylococcus

aureus

OOID

ELISA

21553

GNYN

YKY

RYLR

HGK

LRPF

ERMus

musculus

SARS

coronavirus

AID

ELISA

003

0032

0021

0031

003

21549

GNYD

FWYQ

SHom

osapiens

Staphylococcus

aureus

OOID

ELISA

21553

GNYN

YKY

RYLR

HGK

LRPF

EROryctolagus

cuniculus

SARS

coronavirus

AID

ELISA

003

0034

0021

0031

003

34908

LAPL

GE

Hom

osapiens

HepatitisE

virus

EWEIR

ELISA

34914

LAPS

TLRS

LRKR

RLSSP

Hom

osapiens

Hum

anherpesvirus

OOID

ELISA

006

006

0059

0063

006

130454

CLFP

NNSY

CMus

musculus

MD

AID

NIAA

130456

CRPQ

VN

NPK

EWS

CAAC

Hom

osapiens

MD

OOD

ACAb

B001

0008

001

0011

0007

1964

4GFV

PSM

Hom

osapiens

Hepatitisd

elta

virus

OOID

ELISA

1964

7GFV

SASI

FGFQ

AEV

GPN

NTR

Oryctolagus

cuniculus

VacciniavirusW

RAID

ELISAminus001minus000

6minus0013minus000

9minus001

20678

GKRP

EMus

musculus

Streptococcus

pyogenes

AID

WI

20680

GKS

KRD

AKN

NAAK

LAVDKL

LMus

musculus

VacciniavirusW

RAID

WI

001

001

0001

001

001

123278

GYL

KDLP

TTOvisa

ries

Fasciolahepatica

AID

ELISA

123282

HAC

QKK

LLKF

EAL

QQEE

GEE

Rattu

snorvegicu

sGallusgallus

AID

PFFminus002minus0016minus0016minus002minus0025

141067

PLSLEP

DP

Mus

musculus

Hom

osapiens

AID

FACS

141073

REGVRW

RVMAIQ

Mus

musculus

Hom

osapiens

AID

ELISA

006

006

006

006

0056

139248

SFAG

TVIE

Mus

musculus

Classicalsw

ine

AID

WI

139305

TAAQ

ITQ

RKWEA

ARE

AEQ

RROryctolagus

cuniculus

Hom

osapiens

AID

IP003

0033

0026

003

003

104515

HDCR

PKKI

Mus

musculus

LaCrossevirus

AID

IFAIH

104520

IAKE

QE

NKE

TIGT

LKKI

LDE

Rattu

snorvegicu

sStreptococcus

pyogenes

AID

ELISAminus006minus006minus0073minus006minus0061

113517

HLY

ADGLT

DMus

musculus

Hum

anpapillomaviru

sAID

IFAIH

113518

HNKI

QA

IELE

DLL

RYS

KLYR

Mus

musculus

Hom

osapiens

AID

ELISA

002

002

0011

002

0019

156970

VER

HQ

Hom

osapiens

Hom

osapiens

OAID

ELISA

156975

WSSTV

LRVS

PTRT

VP

Mus

musculus

MD

AID

ELISA

001

0012

0003

0017

001

78252

PVQNLT

Mus

musculus

Porphyromonas

gingivalis

AID

WI

78253

QGGCG

RGWAFS

ATG

AIEA

Mus

musculus

Glycinem

axAID

ELISA

002

002

0017

002

0018

6068

CCPD

KNKS

Mus

musculus

Hum

anherpesvirus

AID

WI

6074

CCRH

KQKD

VGDVK

QTL

PPS

Ovisa

ries

MD

AID

ELISA

001

000

6000

4001

0008

12 Journal of Immunology Research

Table3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

53109

RAGVC

YHom

osapiens

Triticum

aestivum

OOA

ELISA

53116

RAILTA

FSPA

QDI

WGTS

Oryctolagus

cuniculus

SARS

coronavirus

AID

ELISAminus002minus0016minus0037minus0023minus002

147041

IPEQ

Hom

osapiens

Triticum

aestivum

OOA

WI

147064

KHQGA

QYV

WN

RTA

Hom

osapiens

Bostaurus

OOID

WI

006

006

0047

0057

006

7066

4VQ

VVYD

YQOryctolagus

cuniculus

Treponem

apallidu

mAID

ELISA

70667

VQWMN

RLIAFA

FAG

NHVS

PHom

osapiens

HepatitisC

virus

OOID

ELISA

005

004

6004

10049

005

134343

DLY

IKMus

musculus

Hum

anpapillomaviru

sAID

PhDIP

134344

DMAQ

VTV

GPG

LLGVS

TLMus

musculus

Hom

osapiens

AID

PhDIP

00minus000

90

0

Journal of Immunology Research 13

The first input term is the value 120582(120576119894119895)ref is the scoring

function 120582 of the efficiency of the initial process 120576119894119895(known

solution) The function 120582(120576119894119895)ref = 1 if the 119894th peptide could

experimentally be demonstrated to be a B-epitope in theassay of reference (reference) carried out in the conditions119888119895 120582(120576119894119895)ref = 0 otherwise The variational-perturbation

terms ΔΔ120594119888119895are at the same time terms typical of pertur-

bation theory and moving average (MA) functions used inBox-Jenkin models in time series [60] These new types ofterms account both for the deviation of the electronegativityof all amino acids in the sequence of the new peptidewith respect to the peptide of reference and with respectto all boundary conditions In Table 1 we give the overallclassification results obtainedwith thismodel Speck-Plancheet al [61ndash63] introduced different multitargetmultiplexingQSARmodels that incorporate this type of information basedon MAs The results obtained with the present model areexcellent comparedwith other similarmodels in the literatureuseful for other problems including moving average models[64 65] or perturbation models [58] Notably this is also thefirst model combining both perturbation theory and MAs ina QSPR context

The other input terms are the following The first Δ120594seq =120594(119898119902)ref minus120594(119898119894)new is the perturbation term for the variation

or in themean value of electronegativity for all amino acids inthe sequence of the peptide of reference The remnant inputvariables of the model ΔΔ120594

119888119895= Δ120594

119888119895-ref minus Δ120594119888119895-new =

[120594(119898119902)ref minus

lowast120594 (119888119902119903)ref] minus [120594(119898119894)new minus

lowast120594 (119888119894119895)new] quantify

values of the conditions of the new assay 119888119895-new that representperturbations with respect to the initial conditions 119888

119894119895-ref of

the assay of reference The quantities lowast120594(119888119894119895) and lowast120594(119888

119902119903) are

the average values of the mean electronegativity values 120594(119898119894)

and 120594(119898119902) for all new and reference peptides in IEDB that

are epitopes under the 119895th or 119903th boundary condition Thevalues of these terms have been tabulated for gt500 sourceorganisms gt50 host organisms gt10 biological process andgt30 experimental techniques We must substitute the valuesof 120594(119898

119894) and 120594(119898

119902) of the new and reference peptides and the

tabulated values of lowast120594(119888119894119895) and lowast120594(119888

119902119903) for all combinations

of boundary conditions to predict the perturbations of theaction as epitope of peptides In doing so we can found theoptimal sequence and boundary conditions towards the useof the peptide in the development of a vaccine In Table 2 wegive some of these values of lowast120594(119888

119894119895) and lowast120594(119888

119902119903)

In Table 3 we depict the sequences and input-outputboundary conditions for top perturbations present in IEDBAll these perturbations have observed value of 120582(120576

119894119895)new = 1

and predicted value also equal to 1 with a high probabilitySee Supplementary Material available online at httpdxdoiorg1011552014768515 file contains a full list of gt200000cases of perturbations

4 Conclusions

It is possible to develop generalmodels for vaccine design ableto predict the results of multiple input-output perturbations

in peptide sequence and experimental assay boundary condi-tions using ideas of QSPR analysis perturbation theory andBox and Jenkins MA operators The electronegativity valuescalculated withMARCH-INSIDE seem to be goodmoleculardescriptors for this type of QSPR-perturbation models

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

The present study was partially supported by GrantsAGL2010-22290-C02 and AGL2011-30563-C03 from Minis-terio de Ciencia e Innovacion Spain and Grant CN 2012155from Xunta de Galicia Spain

References

[1] B Peters J Sidney P Bourne et al ldquoThe design and implemen-tation of the immune epitope database and analysis resourcerdquoImmunogenetics vol 57 no 5 pp 326ndash336 2005

[2] B Peters J Sidney P Bourne et al ldquoThe immune epitopedatabase and analysis resource from vision to blueprintrdquo PLoSBiology vol 3 no 3 p e91 2005

[3] PWang A AMorgan Q Zhang A Sette and B Peters ldquoAuto-mating document classification for the Immune Epitope Data-baserdquo BMC Bioinformatics vol 8 article 269 2007

[4] A Sette ldquoThe immune epitope database and analysis resourcefrom vision to blueprintrdquo Genome Informatics vol 15 no 2 p299 2004

[5] N Salimi W Fleri B Peters and A Sette ldquoThe immune epi-tope database a historical retrospective of the first decaderdquoImmunology vol 137 no 2 pp 117ndash123 2012

[6] Y Kim A Sette and B Peters ldquoApplications for T-cell epitopequeries and tools in the Immune EpitopeDatabase andAnalysisResourcerdquo Journal of Immunological Methods vol 374 no 1-2pp 62ndash69 2011

[7] Y Kim J Ponomarenko Z Zhu D Tamang et al ldquoImmuneepitope database analysis resourcerdquo Nucleic Acids Research ppW525ndashW530 2012

[8] A M Helguera R D Combes M P Gonzalez and M N DS Cordeiro ldquoApplications of 2D descriptors in drug design aDRAGON talerdquo Current Topics in Medicinal Chemistry vol 8no 18 pp 1628ndash1655 2008

[9] GM Casanola-Martın Y Marrero-Ponce M T H Khan et alldquoDragon method for finding novel tyrosinase inhibitors Biosil-ico identification and experimental in vitro assaysrdquo The Euro-pean Journal of Medicinal Chemistry vol 42 no 11-12 pp 1370ndash1381 2007

[10] I V Tetko J Gasteiger R Todeschini et al ldquoVirtual computa-tional chemistry laboratorymdashdesign and descriptionrdquo Journalof Computer-AidedMolecular Design vol 19 no 6 pp 453ndash4632005

[11] A R Katritzky A Oliferenko A Lomaka and M KarelsonldquoSix-membered cyclic ureas as HIV-1 protease inhibitors aQSAR study based on CODESSA PRO approachrdquo Bioorganicand Medicinal Chemistry Letters vol 12 no 23 pp 3453ndash34572002

14 Journal of Immunology Research

[12] A R Katritzky S Perumal R Petrukhin and E Klein-peter ldquoCODESSA-based theoretical QSPRmodel for hydantoinHPLC-RT lipophilicitiesrdquo Journal of Chemical Information andComputer Sciences vol 41 no 3 pp 569ndash574 2001

[13] S Vilar G Cozza and S Moro ldquoMedicinal chemistry and themolecular operating environment (MOE) application of QSARand molecular docking to drug discoveryrdquo Current Topics inMedicinal Chemistry vol 8 no 18 pp 1555ndash1572 2008

[14] G Marzaro A Chilin A Guiotto et al ldquoUsing the TOPS-MODE approach to fit multi-target QSAR models for tyrosinekinases inhibitorsrdquo The European Journal of Medicinal Chem-istry vol 46 no 6 pp 2185ndash2192 2011

[15] S Vilar E Estrada E Uriarte L Santana and Y Gutierrez ldquoInsilico studies toward the discovery of new anti-HIV nucleosidecompounds through the use of tops-mode and 2D3D con-nectivity indices 2 Purine derivativesrdquo Journal of ChemicalInformation and Modeling vol 45 no 2 pp 502ndash514 2005

[16] E Estrada J A Quincoces and G Patlewicz ldquoCreating molec-ular diversity from antioxidants in Brazilian propolis combina-tion of TOPS-MODE QSAR and virtual structure generationrdquoMolecular Diversity vol 8 no 1 pp 21ndash33 2004

[17] E Estrada andHGonzalez ldquoWhat are the limits of applicabilityfor graph theoretic descriptors in QSPRQSAR modelingdipole moments of aromatic compounds with TOPS-MODEdescriptorsrdquo Journal of Chemical Information and ComputerSciences vol 43 no 1 pp 75ndash84 2003

[18] Y Marrero-Ponce J A Castillo-Garit E Olazabal et al ldquoTom-ocomd-Cardd a novel approach for computer-aided ldquorationalrdquodrug design I Theoretical and experimental assessment of apromising method for computational screening and in silicodesign of new anthelmintic compoundsrdquo Journal of Computer-Aided Molecular Design vol 18 no 10 pp 615ndash634 2004

[19] Y Marrero Ponce R Medina Marrero E A Castro et al ldquoPro-tein quadratic indices of the ldquomacromolecular pseudographrsquos 120572-carbon atom adjacency matrixrdquo 1 prediction of arc repressoralanine-mutantrsquos stabilityrdquoMolecules vol 9 no 12 pp 1124ndash11472004

[20] H Gonzalez-Dıaz F Prado-Prado and F M Ubeira ldquoPredict-ing antimicrobial drugs and targets with the MARCH-INSIDEapproachrdquo Current Topics in Medicinal Chemistry vol 8 no 18pp 1676ndash1690 2008

[21] R Garcıa-Domenech J Galvez J V de Julian-Ortiz and LPogliani ldquoSomenew trends in chemical graph theoryrdquoChemicalReviews vol 108 no 3 pp 1127ndash1169 2008

[22] E Estrada and E Uriarte ldquoRecent advances on the role of topo-logical indices in drug discovery researchrdquo Current MedicinalChemistry vol 8 no 13 pp 1573ndash1588 2001

[23] R Todeschini andVConsonniHandbook ofMolecularDescrip-tors Wiley-VCH Weinheim Germany 2008

[24] E Estrada E J Delgado J B Alderete and G A Jana ldquoQuan-tum-connectivity descriptors inmodeling solubility of environ-mentally important organic compoundsrdquo Journal of Computa-tional Chemistry vol 25 no 14 pp 1787ndash1796 2004

[25] E Besalu X Girones L Amat andR Carbo-Dorca ldquoMolecularquantum similarity and the fundamentals of QSARrdquo Accountsof Chemical Research vol 35 no 5 pp 289ndash295 2002

[26] D A Rincon M N D S Cordeiro and R A Mosquera ldquoOnthe electronic structure of cocaine and its metabolitesrdquo Journalof Physical Chemistry A vol 113 no 50 pp 13937ndash13942 2009

[27] M Mandado M J Gonzalez-Moa and R A MosqueraldquoChemical graph theory and n-center electron delocalization

indices a study on polycyclic aromatic hydrocarbonsrdquo Journalof Computational Chemistry vol 28 no 10 pp 1625ndash1633 2007

[28] THill andP Lewicki STATISTICSMethods andApplications AComprehensive Reference for Science Industry and DataMiningStatSoft Tulsa Okla USA 2006

[29] E Frank M Hall L Trigg G Holmes and I H Witten ldquoDatamining in bioinformatics using Wekardquo Bioinformatics vol 20no 15 pp 2479ndash2481 2004

[30] G Patlewicz N Ball E D Booth E Hulzebos E Zvinavasheand C Hennes ldquoUse of category approaches read-across and(Q)SAR general considerationsrdquo Regulatory Toxicology andPharmacology vol 67 no 1 pp 1ndash12 2013

[31] D W Roberts and G Y Patlewicz ldquoUpdating the skin sensiti-zation in vitro data assessment paradigm in 2009mdasha chemistryand QSAR perspectiverdquo Journal of Applied Toxicology vol 30no 3 pp 286ndash288 2010

[32] D W Roberts and G Y Patlewicz ldquoNonanimal alternatives forskin sensitization letter to the editorrdquoToxicological Sciences vol106 no 2 pp 572ndash574 2008

[33] E Estrada G Patlewicz and Y Gutierrez ldquoFrom knowledgegeneration to knowledge archive a general strategy usingTOPS-MODE with DEREK to formulate new alerts for skinsensitizationrdquo Journal of Chemical Information and ComputerSciences vol 44 no 2 pp 688ndash698 2004

[34] G F Gerberick C A Ryan P S Kern et al ldquoA chemical datasetfor evaluation of alternative approaches to skin-sensitizationtestingrdquo Contact Dermatitis vol 50 no 5 pp 274ndash288 2004

[35] G Y Patlewicz D A Basketter C K Smith Pease et al ldquoFurtherevaluation of quantitative structurendashactivity relationship mod-els for the prediction of the skin sensitization potency ofselected fragrance allergensrdquo Contact Dermatitis vol 50 no 2pp 91ndash97 2004

[36] E Estrada G Patlewicz M Chamberlain D Basketter andS Larbey ldquoComputer-aided knowledge generation for under-standing skin sensitization mechanisms the TOPS-MODEapproachrdquo Chemical Research in Toxicology vol 16 no 10 pp1226ndash1235 2003

[37] G Patlewicz R Rodford and J D Walker ldquoQuantitative struc-ture-activity relationships for predicting skin and eye irritationrdquoEnvironmental Toxicology and Chemistry vol 22 no 8 pp1862ndash1869 2003

[38] G Y Patlewicz R A Rodford G Ellis and M D Barratt ldquoAQSAR model for the eye irritation of cationic surfactantsrdquo Tox-icology In Vitro vol 14 no 1 pp 79ndash84 2000

[39] E Tenorio-Borroto C G Penuelas Rivas J C VasquezChagoyan N Castanedo F J Prado-Prado and X Garcia-Mera ldquoANN multiplexing model of drugs effect on macro-phages theoretical and flow cytometry study on the cyto-toxicity of the anti-microbial drug G1 in spleenrdquo Bioorganic ampMedicinal Chemistry vol 20 no 20 pp 6181ndash6194 2012

[40] D R Flower H McSparron M J Blythe et al ldquoComputationalvaccinology quantitative approachesrdquo Novartis FoundationSymposium vol 254 pp 102ndash120 2003

[41] I A Doytchinova P Guan and D R Flower ldquoQuantitativestructure-activity relationships and the prediction of MHCsupermotifsrdquoMethods vol 34 no 4 pp 444ndash453 2004

[42] Y Xiao and M R Segal ldquoPrediction of genomewide conservedepitope profiles of HIV-1 classifier choice and peptide rep-resentationrdquo Statistical Applications in Genetics and MolecularBiology vol 4 no 1 article 25 2005

Journal of Immunology Research 15

[43] T Fagerberg V Zoete S Viatte P Baumgaertner P M Alves PRomero et al ldquoPrediction of cross-recognition of peptide-HLAA2 by melan-a-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationshipsrdquo PLoSONE vol 8 no 7 Article ID e65590 2013

[44] D Barh A N Misra A Kumar and A Vasco ldquoA novel strategyof epitope design inNeisseria gonorrhoeaerdquo Bioinformation vol5 no 2 pp 77ndash82 2010

[45] J Bi R Song H Yang et al ldquoStepwise identification of HLA-Alowast0201-restricted CD8+ T-cell epitope peptides from herpessimplex virus type 1 genome boosted by a steprank schemerdquoBiopolymers vol 96 no 3 pp 328ndash339 2011

[46] R D Bremel and E J Homan ldquoAn integrated approach to epit-ope analysis II a system for proteomic-scale prediction ofimmunological characteristicsrdquo Immunome Research vol 6 no1 article 8 2010

[47] C M Diez-Rivero B Chenlo P Zuluaga and P A RecheldquoQuantitative modeling of peptide binding to TAP using sup-port vector machinerdquo Proteins vol 78 no 1 pp 63ndash72 2010

[48] E Martınez-Naves E M Lafuente and P A Reche ldquoRecogni-tion of the ligand-type specificity of classical and non-classicalMHC I proteinsrdquo FEBS Letters vol 585 no 21 pp 3478ndash34842011

[49] M Bhasin E L Reinherz and P A Reche ldquoRecognition andclassification of histones using support vectormachinerdquo Journalof Computational Biology vol 13 no 1 pp 102ndash112 2006

[50] P A Reche and E L Reinherz ldquoPEPVAC a web server formulti-epitope vaccine development based on the prediction ofsupertypic MHC ligandsrdquo Nucleic Acids Research vol 33 no 2pp W138ndashW142 2005

[51] E M Lafuente and P A Reche ldquoPrediction of MHC-peptidebinding a systematic and comprehensive overviewrdquo CurrentPharmaceutical Design vol 15 no 28 pp 3209ndash3220 2009

[52] A Gaulton L J Bellis A P Bento et al et al ldquoA large-scalebioactivity database for drug discoveryrdquoNucleic Acids Researchpp D1100ndashD1107 2012

[53] W H Cropper Great Physicists The Life and Times of LeadingPhysicists from Galileo to Hawking Oxford University PressNew York NY USA 2004

[54] E L Bouzarth A Brooks R Camassa et al ldquoEpicyclic orbits ina viscous fluid about a precessing rod theory and experimentsat the micro-and macro-scalesrdquo Physical Review E vol 76 no1 part 2 Article ID 016313 2007

[55] M Laberge ldquoIntrinsic protein electric fields basic non-covalentinteractions and relationship to protein-induced Stark effectsrdquoBiochimica et BiophysicaActa vol 1386 no 2 pp 305ndash330 1998

[56] FM Fernandez and J AMorales ldquoPerturbation theorywithoutwave functions for the Zeeman effect in hydrogenrdquo PhysicalReview A vol 46 no 1 pp 318ndash326 1992

[57] B D Marshall and W G Chapman ldquoThermodynamic pertur-bation theory for associating fluids with small bond angleseffects of steric hindrance ring formation and double bondingrdquoPhysical Reiew E vol 87 no 5 Article ID 052307 12 pages 2013

[58] H Gonzalez-Diaz S Arrasate A Gomez-San Juan et al ldquoNewtheory for multiple input-output perturbations in complexmolecular systems 1 Linear QSPR electronegativity models inphysical organic and medicinal chemistryrdquo Current Topics inMedicinal Chemistry 2013

[59] H Gonzalez-Diaz S Arrasate N Sotomayor et al ldquoMIANNmodels in medicinal physical and organic chemistryrdquo CurrentTopics in Medicinal Chemistry vol 13 no 5 pp 619ndash641 2013

[60] G E P Box and GM Jenkins Time Series Analysis Forecastingand Control Holden-Day San Francisco Calif USA 1970

[61] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoUnified multi-target approach for the rational insilico design of anti-bladder cancer agentsrdquo Anti-Cancer Agentsin Medicial Chemistry vol 13 no 5 pp 791ndash800 2013

[62] A Speck-Planche V V Kleandrova and M N Cordeiro ldquoNewinsights toward the discovery of antibacterial agents multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugsrdquo TheEuropean Journal of Pharmaceutical Sciences vol 48 no 4-5pp 812ndash818 2013

[63] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoMulti-target inhibitors for proteins associated withAlzheimer in silico discovery using fragment-based descrip-torsrdquo Current Alzheimer Research vol 10 no 2 pp 117ndash1242013

[64] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoIn silico discovery and virtual screening of multi-target inhibitors for proteins in Mycobacterium tuberculosisrdquoCombinatorial Chemistry amp High Throughput Screening vol 15no 8 pp 666ndash673 2012

[65] A Speck-Planche V V Kleandrova F Luan and M N DS Cordeiro ldquoChemoinformatics in anti-cancer chemotherapymulti-target QSAR model for the in silico discovery of anti-breast cancer agentsrdquo The European Journal of PharmaceuticalSciences vol 47 no 1 pp 273ndash279 2012

Submit your manuscripts athttpwwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MEDIATORSINFLAMMATION

of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Behavioural Neurology

EndocrinologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Disease Markers

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

OncologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Oxidative Medicine and Cellular Longevity

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational and Mathematical Methods in Medicine

OphthalmologyJournal of

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Diabetes ResearchJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Research and TreatmentAIDS

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Gastroenterology Research and Practice

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Page 8: Research Article Model for Vaccine Design by …downloads.hindawi.com/journals/jir/2014/768515.pdfResearch Article Model for Vaccine Design by Prediction of B-Epitopes of IEDB Given

8 Journal of Immunology Research

Table3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

50990

QGYR

VSSY

LPHom

osapiens

Hevea

brasiliensis

OOA

WI

50998

QHEQ

DR

PTPS

PAP

SRPF

SVL

Hom

osapiens

HepatitisE

virus

OOID

ELISA

001

001minus0002

0007

0008

100458

RDVLQ

LYAPE

Mus

musculus

Bacillusa

nthracis

AID

ELISA

1004

62RF

STRY

GNQNGRI

RVLQ

RFD

Hom

osapiens

Arachish

ypogaea

EWED

ELISA

003

0028

0018

003

003

111036

TEST

FTGEA

YSV

Hom

osapiens

Mycoplasm

apenetra

nsEW

EIR

ELISA

111039

TGVPID

PAVPD

SSIV

PLLE

SBo

staurus

Bovine

papillomaviru

sAID

ELISA

003

0035

002

0033

003

117919

IFIEME

Hom

osapiens

Hom

osapiens

OAID

WI

117921

IGIIDLIE

KRKF

NQ

Mus

musculus

Hom

osapiens

AID

WI

003

0032

003

0037

003

7127

CTDTD

KLF

Oryctolagus

cuniculus

Shigellaflexneri

AID

ELISA

7128

CTDVS

TAIH

ADQL

TPAW

Hom

osapiens

SARS

coronavirus

OOID

ELISA

001

000

6minus0003

000

9001

112253

PGQSP

KLHom

osapiens

Hom

osapiens

OAID

ELISA

112255

PIRA

LVGDEV

ELP

CRISPG

KMus

musculus

Hom

osapiens

AID

ELISA

001

0012

001

0017

001

122034

WNPA

DRa

ttus

norvegicu

sTorpedocalifornica

AID

ELISA

122038

WNPD

DY

GGVKW

NP

DDYG

GVK

Rattu

snorvegicu

sMD

AID

RIA

00minus000

90minus0001

131878

FLMLV

GGST

LHom

osapiens

Hom

osapiens

OAID

ACAb

B131879

FLVA

HT

RARA

PSA

GER

ARR

SMus

musculus

Mus

musculus

AID

NIAA

003

0032

0028

0037

0033

7066

4VQ

VVYD

YQHom

osapiens

Treponem

apallidu

mOOID

ELISA

70667

VQWMNR

LIAFA

FAG

NHVS

PHom

osapiens

HepatitisC

virus

OOID

ELISA

005

005

004

1005

005

71545

VTV

Hom

osapiens

Helicobacterpylori

OOID

ELISA

71559

VTV

RGGL

RILSPD

RKHom

osapiens

Arachish

ypogaea

OOA

WI

004

004

0032

0043

004

2

127856

TDVRY

KD

Mus

musculus

Mus

musculus

AID

ACAb

B127857

TDVRY

KDDMYH

FFCP

AIQ

AQMus

musculus

Mus

musculus

AID

PFF

001

001

001

001

000

6

112149

GVG

WIRQ

Hom

osapiens

Hom

osapiens

OAID

ELISA

112152

HHPA

RTAHYG

SLP

QKS

HGRT

Hom

osapiens

Hom

osapiens

AID

ELISA

00

00007

0

119581

FSCS

VMHE

Hom

osapiens

Hom

osapiens

OAID

ELISA

119582

GLQ

LIQL

INVDEV

NQI

Mus

musculus

Hom

osapiens

AID

RIAminus001minus0008minus001minus0003minus0011

144657

GQITVD

MMYG

Hom

osapiens

Hom

osapiens

OAID

ELISA

144659

GRE

GYP

ADGGAA

GYC

NTE

Oryctolagus

cuniculus

MD

AID

WIminus001minus000

6minus0017minus0003minus0008

25013

HVA

DI

DKL

IDMus

musculus

Puum

alavirus

Kazan

AID

ELISA

25022

HVA

PTH

YVVES

DA

SQRV

TQV

Hom

osapiens

HepatitisC

virus

OOID

ELISA

0minus0002minus0014minus0001

0

Journal of Immunology Research 9Ta

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

144652

GMRG

MKG

LVY

Hom

osapiens

Hom

osapiens

OAID

ELISA

144654

GPH

PTLE

VVPM

GRG

SMus

musculus

MD

AID

ELISAminus002minus0018minus0027minus0013minus002

104515

HDCR

PKKI

Mus

musculus

LaCrossevirus

AID

IFAIH

104521

IGTL

KKIL

DET

VKD

KIAKE

QRa

ttus

norvegicu

sStreptococcus

pyogenes

AID

ELISAminus004minus004minus0053minus004minus004

1

7367

CYGDWA

Hom

osapiens

Triticum

aestivum

OOA

ELISA

7374

CYGLP

DS

EPTK

TNGK

Mus

musculus

Tityus

serrulatus

AID

WIminus002minus0018minus0043minus0023minus0018

7367

CYGDWA

Hom

osapiens

Triticum

aestivum

OOA

ELISA

7374

CYGLP

DS

EPTK

TNGK

Mus

musculus

Tityus

serrulatus

AID

WIminus002minus0018minus0043minus0023minus0018

144610

DFF

TYK

Mus

musculus

Porcine

transmissible

AID

WI

144611

DFN

GSF

DMNGTITA

Oryctolagus

cuniculus

Escherich

iacoli

AID

ELISAminus001minus0007minus0018minus001minus0012

112047

AST

RESG

Hom

osapiens

Hom

osapiens

OAID

ELISA

112048

ATAST

MDHARH

GF

LPRH

RDT

Hom

osapiens

Hom

osapiens

AID

ELISA

005

005

005

0057

005

36136

LGGVFT

Hom

osapiens

Denguev

irus2

OOID

ELISA

36137

LGGWKL

QSD

PRAY

AL

Hom

osapiens

Ambrosia

artemisiifolia

OOA

RIA

001

001

0007

0013

000

9

115256

FREL

KDLK

GY

Hom

osapiens

Bostaurus

DEW

EDWI

115261

GDLE

ILL

QKW

ENG

ECAQ

KKI

Hom

osapiens

Bostaurus

OOA

FIAminus001minus001minus001

0minus000

9

129024

KADQLY

KHom

osapiens

Hom

osapiens

OAID

ELISA

129026

KAKK

PAAAAG

AKK

AKS

Oryctolagus

cuniculus

Hom

osapiens

AID

ELISA

003

0034

003

0037

003

148481

YTRD

LVYK

Rattu

snorvegicu

sHom

osapiens

AID

WI

148483

YVPIVT

FYSE

ISM

HSSRA

IPOryctolagus

cuniculus

MD

AID

ELISA

00002minus0007

0minus0002

150850

GY

Mus

musculus

Hum

anpapillomaviru

sAID

ELISA

150853

HIG

GLSI

LDPIFG

VL

Hom

osapiens

Dermatophagoides

farin

aeOOA

ACAb

B004

0038

004

0043

0039

107366

FPPK

PKD

Hom

osapiens

Hom

osapiens

OAID

ELISA

107376

GDRS

GYS

SPGSP

GMus

musculus

Hom

osapiens

AID

ACAb

Bminus003minus0028minus003minus0023minus0031

114859

ICGTD

GVTY

THom

osapiens

Gallusgallus

OOA

WI

114865

IVER

ETR

GQSE

NPL

WHALR

RRa

ttus

norvegicu

sHum

anherpesvirus

AID

ELISA

004

004

20035

0037

0038

62149

SVHLF

Hom

osapiens

MD

OAID

WI

62150

SVIALG

SQEG

ALH

QALA

GAI

Equu

scaballus

WestN

ilevirus

AID

IFAIHminus002minus0021minus0012minus0013minus0021

98455

SGSV

QFV

PIQ

Mus

musculus

Neisseria

meningitid

isAID

ELISA

98456

SICS

NNP

TCWAICK

RIPN

KKMus

musculus

Hum

anrespira

tory

virus

AID

IFAIH

004

004

0027

004

004

1

61783

STNKA

VVS

LSBo

staurus

Bovine

respira

tory

AID

ELISA

61791

STNPK

PQRK

TKRN

TNRR

PQHom

osapiens

HepatitisC

virus

EWEIR

ACAb

B004

0035

0029

0037

0039

100318

NAPK

TFQ

FIN

Mus

musculus

Bacillusa

nthracis

AID

ELISA

100319

NASSEL

HLL

GFG

INAEN

NHR

Hom

osapiens

Arachish

ypogaea

EWED

ELISA

0minus0002minus0012

00

10 Journal of Immunology ResearchTa

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

118947

PFSA

PPPA

Hom

osapiens

Hom

osapiens

OAID

ELISA

118948

PGAIEQG

PADDPG

EGPS

TGP

Hom

osapiens

Hum

anherpesvirus

NI

ACAb

Bminus004minus004minus004

1minus0036minus004

1

78323

YSFR

DMus

musculus

Bluetongue

virus1

AID

ELISA

78341

AALT

AEN

TAIK

KRN

ADAKA

Hom

osapiens

Streptococcus

mutan

sEE

EEL

ISA

001

0008

0007

0013

001

145831

IPLG

TRP

Mus

musculus

Hum

anpapillomaviru

sAID

ELISA

145841

KEDFR

YAISST

NEI

GLL

GA

Susscrofa

Classicalsw

ine

AID

PACminus004minus004minus0045minus004minus0043

119592

HTF

PAVLQ

Hom

osapiens

Hom

osapiens

OAID

ELISA

119596

IHIPSE

KIWRP

DLV

LYMus

musculus

Hom

osapiens

AID

RIA

001

0012

001

0017

000

9

58780

SKAANLSIIK

MD

Beetnecrotic

MD

WI

58783

SKAFSN

CYPY

DVP

DYA

SLOryctolagus

cuniculus

Influ

enza

Avirus

AID

RIAminus001minus000

4minus0014minus000

9minus0013

115153

MKG

VVC

TRIYEK

VHom

osapiens

Hom

osapiens

NI

ELISA

115155

NNQRK

KAKN

TPFN

MLK

RERN

Mus

musculus

Denguev

irus2

AID

ELISA

001

0012

000

40013

001

133629

LPLR

FOryctolagus

cuniculus

Gallusgallus

AID

ACAb

B133630

LPPG

LHV

FPLA

SNRS

Mus

musculus

MD

AID

SPRminus001minus0013minus0021minus001minus001

96215

EEEE

AE

DKE

DHom

osapiens

Hom

osapiens

OAID

ELISA

96216

EEEG

LLK

KSADTL

WNMQK

Mus

musculus

Mus

musculus

AID

ELISA

008

0082

0078

0087

008

39782

LTAASV

Hom

osapiens

Triticum

aestivum

OOA

ELISA

39788

LTAEL

KIYS

VIQ

AEI

NKH

LOryctolagus

cuniculus

Yersiniapestis

AID

ACAb

B001

0014minus0001

0007

000

9

107479

KFNWYV

DHom

osapiens

Hom

osapiens

OAID

ELISA

107482

KGEP

GL

PGRG

FPGFP

Mus

musculus

Hom

osapiens

AID

ACAb

B0

0002

00007minus0001

63569

TETV

NSD

IMacaca

mulatta

Shigellaflexneri

AID

ELISA

63573

TEVEL

KER

KHRIED

AVRN

AK

Hom

osapiens

Mycobacteriu

mlep

rae

OOID

ELISA

005

0036

004

10049

005

134028

DDTIS

Mus

musculus

Hom

osapiens

AID

NIAA

134029

DED

ENQS

PRSFQKK

TROryctolagus

cuniculus

Hom

osapiens

AID

ELISA

005

0053

005

005

004

8

23028

GVKY

AHom

osapiens

MD

OAID

WI

23032

GVLA

KDV

RFSQ

VHom

osapiens

MD

EWEIR

ACAb

B0

00

000

4minus0003

115293

IMCV

KKILDK

Hom

osapiens

Bostaurus

DEW

EDWI

115295

INPS

KEN

LCST

FCK

EVVRN

AHom

osapiens

Bostaurus

OOA

FIAminus003minus003minus003minus002minus0029

65105

TLTP

ENTL

Mus

musculus

Shigellaflexneri

AID

ELISA

65110

TLTS

GSD

LDRC

TTFD

DV

Oryctolagus

cuniculus

SARS

coronavirus

AID

ELISA

001

0013minus0003

001

001

1344

71PK

PEQ

Mus

musculus

Streptococcus

pneumoniae

AID

FACS

1344

72PL

LPGT

STTS

TGPC

KTHom

osapiens

HepatitisB

virus

AID

ELISA

002

0018

0019

002

0016

Journal of Immunology Research 11Ta

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

142228

LYCY

EQLN

DSSE

Hom

osapiens

Hum

anpapillomaviru

sNI

ELISA

142250

NWGDEP

SKRR

DRS

NSR

GRK

NFelis

catus

Felin

einfectio

usAID

ELISA

007

006

80071

0073

007

21549

GNYD

FWYQ

SHom

osapiens

Staphylococcus

aureus

OOID

ELISA

21553

GNYN

YKY

RYLR

HGK

LRPF

ERMus

musculus

SARS

coronavirus

AID

ELISA

003

0032

0021

0031

003

21549

GNYD

FWYQ

SHom

osapiens

Staphylococcus

aureus

OOID

ELISA

21553

GNYN

YKY

RYLR

HGK

LRPF

EROryctolagus

cuniculus

SARS

coronavirus

AID

ELISA

003

0034

0021

0031

003

34908

LAPL

GE

Hom

osapiens

HepatitisE

virus

EWEIR

ELISA

34914

LAPS

TLRS

LRKR

RLSSP

Hom

osapiens

Hum

anherpesvirus

OOID

ELISA

006

006

0059

0063

006

130454

CLFP

NNSY

CMus

musculus

MD

AID

NIAA

130456

CRPQ

VN

NPK

EWS

CAAC

Hom

osapiens

MD

OOD

ACAb

B001

0008

001

0011

0007

1964

4GFV

PSM

Hom

osapiens

Hepatitisd

elta

virus

OOID

ELISA

1964

7GFV

SASI

FGFQ

AEV

GPN

NTR

Oryctolagus

cuniculus

VacciniavirusW

RAID

ELISAminus001minus000

6minus0013minus000

9minus001

20678

GKRP

EMus

musculus

Streptococcus

pyogenes

AID

WI

20680

GKS

KRD

AKN

NAAK

LAVDKL

LMus

musculus

VacciniavirusW

RAID

WI

001

001

0001

001

001

123278

GYL

KDLP

TTOvisa

ries

Fasciolahepatica

AID

ELISA

123282

HAC

QKK

LLKF

EAL

QQEE

GEE

Rattu

snorvegicu

sGallusgallus

AID

PFFminus002minus0016minus0016minus002minus0025

141067

PLSLEP

DP

Mus

musculus

Hom

osapiens

AID

FACS

141073

REGVRW

RVMAIQ

Mus

musculus

Hom

osapiens

AID

ELISA

006

006

006

006

0056

139248

SFAG

TVIE

Mus

musculus

Classicalsw

ine

AID

WI

139305

TAAQ

ITQ

RKWEA

ARE

AEQ

RROryctolagus

cuniculus

Hom

osapiens

AID

IP003

0033

0026

003

003

104515

HDCR

PKKI

Mus

musculus

LaCrossevirus

AID

IFAIH

104520

IAKE

QE

NKE

TIGT

LKKI

LDE

Rattu

snorvegicu

sStreptococcus

pyogenes

AID

ELISAminus006minus006minus0073minus006minus0061

113517

HLY

ADGLT

DMus

musculus

Hum

anpapillomaviru

sAID

IFAIH

113518

HNKI

QA

IELE

DLL

RYS

KLYR

Mus

musculus

Hom

osapiens

AID

ELISA

002

002

0011

002

0019

156970

VER

HQ

Hom

osapiens

Hom

osapiens

OAID

ELISA

156975

WSSTV

LRVS

PTRT

VP

Mus

musculus

MD

AID

ELISA

001

0012

0003

0017

001

78252

PVQNLT

Mus

musculus

Porphyromonas

gingivalis

AID

WI

78253

QGGCG

RGWAFS

ATG

AIEA

Mus

musculus

Glycinem

axAID

ELISA

002

002

0017

002

0018

6068

CCPD

KNKS

Mus

musculus

Hum

anherpesvirus

AID

WI

6074

CCRH

KQKD

VGDVK

QTL

PPS

Ovisa

ries

MD

AID

ELISA

001

000

6000

4001

0008

12 Journal of Immunology Research

Table3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

53109

RAGVC

YHom

osapiens

Triticum

aestivum

OOA

ELISA

53116

RAILTA

FSPA

QDI

WGTS

Oryctolagus

cuniculus

SARS

coronavirus

AID

ELISAminus002minus0016minus0037minus0023minus002

147041

IPEQ

Hom

osapiens

Triticum

aestivum

OOA

WI

147064

KHQGA

QYV

WN

RTA

Hom

osapiens

Bostaurus

OOID

WI

006

006

0047

0057

006

7066

4VQ

VVYD

YQOryctolagus

cuniculus

Treponem

apallidu

mAID

ELISA

70667

VQWMN

RLIAFA

FAG

NHVS

PHom

osapiens

HepatitisC

virus

OOID

ELISA

005

004

6004

10049

005

134343

DLY

IKMus

musculus

Hum

anpapillomaviru

sAID

PhDIP

134344

DMAQ

VTV

GPG

LLGVS

TLMus

musculus

Hom

osapiens

AID

PhDIP

00minus000

90

0

Journal of Immunology Research 13

The first input term is the value 120582(120576119894119895)ref is the scoring

function 120582 of the efficiency of the initial process 120576119894119895(known

solution) The function 120582(120576119894119895)ref = 1 if the 119894th peptide could

experimentally be demonstrated to be a B-epitope in theassay of reference (reference) carried out in the conditions119888119895 120582(120576119894119895)ref = 0 otherwise The variational-perturbation

terms ΔΔ120594119888119895are at the same time terms typical of pertur-

bation theory and moving average (MA) functions used inBox-Jenkin models in time series [60] These new types ofterms account both for the deviation of the electronegativityof all amino acids in the sequence of the new peptidewith respect to the peptide of reference and with respectto all boundary conditions In Table 1 we give the overallclassification results obtainedwith thismodel Speck-Plancheet al [61ndash63] introduced different multitargetmultiplexingQSARmodels that incorporate this type of information basedon MAs The results obtained with the present model areexcellent comparedwith other similarmodels in the literatureuseful for other problems including moving average models[64 65] or perturbation models [58] Notably this is also thefirst model combining both perturbation theory and MAs ina QSPR context

The other input terms are the following The first Δ120594seq =120594(119898119902)ref minus120594(119898119894)new is the perturbation term for the variation

or in themean value of electronegativity for all amino acids inthe sequence of the peptide of reference The remnant inputvariables of the model ΔΔ120594

119888119895= Δ120594

119888119895-ref minus Δ120594119888119895-new =

[120594(119898119902)ref minus

lowast120594 (119888119902119903)ref] minus [120594(119898119894)new minus

lowast120594 (119888119894119895)new] quantify

values of the conditions of the new assay 119888119895-new that representperturbations with respect to the initial conditions 119888

119894119895-ref of

the assay of reference The quantities lowast120594(119888119894119895) and lowast120594(119888

119902119903) are

the average values of the mean electronegativity values 120594(119898119894)

and 120594(119898119902) for all new and reference peptides in IEDB that

are epitopes under the 119895th or 119903th boundary condition Thevalues of these terms have been tabulated for gt500 sourceorganisms gt50 host organisms gt10 biological process andgt30 experimental techniques We must substitute the valuesof 120594(119898

119894) and 120594(119898

119902) of the new and reference peptides and the

tabulated values of lowast120594(119888119894119895) and lowast120594(119888

119902119903) for all combinations

of boundary conditions to predict the perturbations of theaction as epitope of peptides In doing so we can found theoptimal sequence and boundary conditions towards the useof the peptide in the development of a vaccine In Table 2 wegive some of these values of lowast120594(119888

119894119895) and lowast120594(119888

119902119903)

In Table 3 we depict the sequences and input-outputboundary conditions for top perturbations present in IEDBAll these perturbations have observed value of 120582(120576

119894119895)new = 1

and predicted value also equal to 1 with a high probabilitySee Supplementary Material available online at httpdxdoiorg1011552014768515 file contains a full list of gt200000cases of perturbations

4 Conclusions

It is possible to develop generalmodels for vaccine design ableto predict the results of multiple input-output perturbations

in peptide sequence and experimental assay boundary condi-tions using ideas of QSPR analysis perturbation theory andBox and Jenkins MA operators The electronegativity valuescalculated withMARCH-INSIDE seem to be goodmoleculardescriptors for this type of QSPR-perturbation models

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

The present study was partially supported by GrantsAGL2010-22290-C02 and AGL2011-30563-C03 from Minis-terio de Ciencia e Innovacion Spain and Grant CN 2012155from Xunta de Galicia Spain

References

[1] B Peters J Sidney P Bourne et al ldquoThe design and implemen-tation of the immune epitope database and analysis resourcerdquoImmunogenetics vol 57 no 5 pp 326ndash336 2005

[2] B Peters J Sidney P Bourne et al ldquoThe immune epitopedatabase and analysis resource from vision to blueprintrdquo PLoSBiology vol 3 no 3 p e91 2005

[3] PWang A AMorgan Q Zhang A Sette and B Peters ldquoAuto-mating document classification for the Immune Epitope Data-baserdquo BMC Bioinformatics vol 8 article 269 2007

[4] A Sette ldquoThe immune epitope database and analysis resourcefrom vision to blueprintrdquo Genome Informatics vol 15 no 2 p299 2004

[5] N Salimi W Fleri B Peters and A Sette ldquoThe immune epi-tope database a historical retrospective of the first decaderdquoImmunology vol 137 no 2 pp 117ndash123 2012

[6] Y Kim A Sette and B Peters ldquoApplications for T-cell epitopequeries and tools in the Immune EpitopeDatabase andAnalysisResourcerdquo Journal of Immunological Methods vol 374 no 1-2pp 62ndash69 2011

[7] Y Kim J Ponomarenko Z Zhu D Tamang et al ldquoImmuneepitope database analysis resourcerdquo Nucleic Acids Research ppW525ndashW530 2012

[8] A M Helguera R D Combes M P Gonzalez and M N DS Cordeiro ldquoApplications of 2D descriptors in drug design aDRAGON talerdquo Current Topics in Medicinal Chemistry vol 8no 18 pp 1628ndash1655 2008

[9] GM Casanola-Martın Y Marrero-Ponce M T H Khan et alldquoDragon method for finding novel tyrosinase inhibitors Biosil-ico identification and experimental in vitro assaysrdquo The Euro-pean Journal of Medicinal Chemistry vol 42 no 11-12 pp 1370ndash1381 2007

[10] I V Tetko J Gasteiger R Todeschini et al ldquoVirtual computa-tional chemistry laboratorymdashdesign and descriptionrdquo Journalof Computer-AidedMolecular Design vol 19 no 6 pp 453ndash4632005

[11] A R Katritzky A Oliferenko A Lomaka and M KarelsonldquoSix-membered cyclic ureas as HIV-1 protease inhibitors aQSAR study based on CODESSA PRO approachrdquo Bioorganicand Medicinal Chemistry Letters vol 12 no 23 pp 3453ndash34572002

14 Journal of Immunology Research

[12] A R Katritzky S Perumal R Petrukhin and E Klein-peter ldquoCODESSA-based theoretical QSPRmodel for hydantoinHPLC-RT lipophilicitiesrdquo Journal of Chemical Information andComputer Sciences vol 41 no 3 pp 569ndash574 2001

[13] S Vilar G Cozza and S Moro ldquoMedicinal chemistry and themolecular operating environment (MOE) application of QSARand molecular docking to drug discoveryrdquo Current Topics inMedicinal Chemistry vol 8 no 18 pp 1555ndash1572 2008

[14] G Marzaro A Chilin A Guiotto et al ldquoUsing the TOPS-MODE approach to fit multi-target QSAR models for tyrosinekinases inhibitorsrdquo The European Journal of Medicinal Chem-istry vol 46 no 6 pp 2185ndash2192 2011

[15] S Vilar E Estrada E Uriarte L Santana and Y Gutierrez ldquoInsilico studies toward the discovery of new anti-HIV nucleosidecompounds through the use of tops-mode and 2D3D con-nectivity indices 2 Purine derivativesrdquo Journal of ChemicalInformation and Modeling vol 45 no 2 pp 502ndash514 2005

[16] E Estrada J A Quincoces and G Patlewicz ldquoCreating molec-ular diversity from antioxidants in Brazilian propolis combina-tion of TOPS-MODE QSAR and virtual structure generationrdquoMolecular Diversity vol 8 no 1 pp 21ndash33 2004

[17] E Estrada andHGonzalez ldquoWhat are the limits of applicabilityfor graph theoretic descriptors in QSPRQSAR modelingdipole moments of aromatic compounds with TOPS-MODEdescriptorsrdquo Journal of Chemical Information and ComputerSciences vol 43 no 1 pp 75ndash84 2003

[18] Y Marrero-Ponce J A Castillo-Garit E Olazabal et al ldquoTom-ocomd-Cardd a novel approach for computer-aided ldquorationalrdquodrug design I Theoretical and experimental assessment of apromising method for computational screening and in silicodesign of new anthelmintic compoundsrdquo Journal of Computer-Aided Molecular Design vol 18 no 10 pp 615ndash634 2004

[19] Y Marrero Ponce R Medina Marrero E A Castro et al ldquoPro-tein quadratic indices of the ldquomacromolecular pseudographrsquos 120572-carbon atom adjacency matrixrdquo 1 prediction of arc repressoralanine-mutantrsquos stabilityrdquoMolecules vol 9 no 12 pp 1124ndash11472004

[20] H Gonzalez-Dıaz F Prado-Prado and F M Ubeira ldquoPredict-ing antimicrobial drugs and targets with the MARCH-INSIDEapproachrdquo Current Topics in Medicinal Chemistry vol 8 no 18pp 1676ndash1690 2008

[21] R Garcıa-Domenech J Galvez J V de Julian-Ortiz and LPogliani ldquoSomenew trends in chemical graph theoryrdquoChemicalReviews vol 108 no 3 pp 1127ndash1169 2008

[22] E Estrada and E Uriarte ldquoRecent advances on the role of topo-logical indices in drug discovery researchrdquo Current MedicinalChemistry vol 8 no 13 pp 1573ndash1588 2001

[23] R Todeschini andVConsonniHandbook ofMolecularDescrip-tors Wiley-VCH Weinheim Germany 2008

[24] E Estrada E J Delgado J B Alderete and G A Jana ldquoQuan-tum-connectivity descriptors inmodeling solubility of environ-mentally important organic compoundsrdquo Journal of Computa-tional Chemistry vol 25 no 14 pp 1787ndash1796 2004

[25] E Besalu X Girones L Amat andR Carbo-Dorca ldquoMolecularquantum similarity and the fundamentals of QSARrdquo Accountsof Chemical Research vol 35 no 5 pp 289ndash295 2002

[26] D A Rincon M N D S Cordeiro and R A Mosquera ldquoOnthe electronic structure of cocaine and its metabolitesrdquo Journalof Physical Chemistry A vol 113 no 50 pp 13937ndash13942 2009

[27] M Mandado M J Gonzalez-Moa and R A MosqueraldquoChemical graph theory and n-center electron delocalization

indices a study on polycyclic aromatic hydrocarbonsrdquo Journalof Computational Chemistry vol 28 no 10 pp 1625ndash1633 2007

[28] THill andP Lewicki STATISTICSMethods andApplications AComprehensive Reference for Science Industry and DataMiningStatSoft Tulsa Okla USA 2006

[29] E Frank M Hall L Trigg G Holmes and I H Witten ldquoDatamining in bioinformatics using Wekardquo Bioinformatics vol 20no 15 pp 2479ndash2481 2004

[30] G Patlewicz N Ball E D Booth E Hulzebos E Zvinavasheand C Hennes ldquoUse of category approaches read-across and(Q)SAR general considerationsrdquo Regulatory Toxicology andPharmacology vol 67 no 1 pp 1ndash12 2013

[31] D W Roberts and G Y Patlewicz ldquoUpdating the skin sensiti-zation in vitro data assessment paradigm in 2009mdasha chemistryand QSAR perspectiverdquo Journal of Applied Toxicology vol 30no 3 pp 286ndash288 2010

[32] D W Roberts and G Y Patlewicz ldquoNonanimal alternatives forskin sensitization letter to the editorrdquoToxicological Sciences vol106 no 2 pp 572ndash574 2008

[33] E Estrada G Patlewicz and Y Gutierrez ldquoFrom knowledgegeneration to knowledge archive a general strategy usingTOPS-MODE with DEREK to formulate new alerts for skinsensitizationrdquo Journal of Chemical Information and ComputerSciences vol 44 no 2 pp 688ndash698 2004

[34] G F Gerberick C A Ryan P S Kern et al ldquoA chemical datasetfor evaluation of alternative approaches to skin-sensitizationtestingrdquo Contact Dermatitis vol 50 no 5 pp 274ndash288 2004

[35] G Y Patlewicz D A Basketter C K Smith Pease et al ldquoFurtherevaluation of quantitative structurendashactivity relationship mod-els for the prediction of the skin sensitization potency ofselected fragrance allergensrdquo Contact Dermatitis vol 50 no 2pp 91ndash97 2004

[36] E Estrada G Patlewicz M Chamberlain D Basketter andS Larbey ldquoComputer-aided knowledge generation for under-standing skin sensitization mechanisms the TOPS-MODEapproachrdquo Chemical Research in Toxicology vol 16 no 10 pp1226ndash1235 2003

[37] G Patlewicz R Rodford and J D Walker ldquoQuantitative struc-ture-activity relationships for predicting skin and eye irritationrdquoEnvironmental Toxicology and Chemistry vol 22 no 8 pp1862ndash1869 2003

[38] G Y Patlewicz R A Rodford G Ellis and M D Barratt ldquoAQSAR model for the eye irritation of cationic surfactantsrdquo Tox-icology In Vitro vol 14 no 1 pp 79ndash84 2000

[39] E Tenorio-Borroto C G Penuelas Rivas J C VasquezChagoyan N Castanedo F J Prado-Prado and X Garcia-Mera ldquoANN multiplexing model of drugs effect on macro-phages theoretical and flow cytometry study on the cyto-toxicity of the anti-microbial drug G1 in spleenrdquo Bioorganic ampMedicinal Chemistry vol 20 no 20 pp 6181ndash6194 2012

[40] D R Flower H McSparron M J Blythe et al ldquoComputationalvaccinology quantitative approachesrdquo Novartis FoundationSymposium vol 254 pp 102ndash120 2003

[41] I A Doytchinova P Guan and D R Flower ldquoQuantitativestructure-activity relationships and the prediction of MHCsupermotifsrdquoMethods vol 34 no 4 pp 444ndash453 2004

[42] Y Xiao and M R Segal ldquoPrediction of genomewide conservedepitope profiles of HIV-1 classifier choice and peptide rep-resentationrdquo Statistical Applications in Genetics and MolecularBiology vol 4 no 1 article 25 2005

Journal of Immunology Research 15

[43] T Fagerberg V Zoete S Viatte P Baumgaertner P M Alves PRomero et al ldquoPrediction of cross-recognition of peptide-HLAA2 by melan-a-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationshipsrdquo PLoSONE vol 8 no 7 Article ID e65590 2013

[44] D Barh A N Misra A Kumar and A Vasco ldquoA novel strategyof epitope design inNeisseria gonorrhoeaerdquo Bioinformation vol5 no 2 pp 77ndash82 2010

[45] J Bi R Song H Yang et al ldquoStepwise identification of HLA-Alowast0201-restricted CD8+ T-cell epitope peptides from herpessimplex virus type 1 genome boosted by a steprank schemerdquoBiopolymers vol 96 no 3 pp 328ndash339 2011

[46] R D Bremel and E J Homan ldquoAn integrated approach to epit-ope analysis II a system for proteomic-scale prediction ofimmunological characteristicsrdquo Immunome Research vol 6 no1 article 8 2010

[47] C M Diez-Rivero B Chenlo P Zuluaga and P A RecheldquoQuantitative modeling of peptide binding to TAP using sup-port vector machinerdquo Proteins vol 78 no 1 pp 63ndash72 2010

[48] E Martınez-Naves E M Lafuente and P A Reche ldquoRecogni-tion of the ligand-type specificity of classical and non-classicalMHC I proteinsrdquo FEBS Letters vol 585 no 21 pp 3478ndash34842011

[49] M Bhasin E L Reinherz and P A Reche ldquoRecognition andclassification of histones using support vectormachinerdquo Journalof Computational Biology vol 13 no 1 pp 102ndash112 2006

[50] P A Reche and E L Reinherz ldquoPEPVAC a web server formulti-epitope vaccine development based on the prediction ofsupertypic MHC ligandsrdquo Nucleic Acids Research vol 33 no 2pp W138ndashW142 2005

[51] E M Lafuente and P A Reche ldquoPrediction of MHC-peptidebinding a systematic and comprehensive overviewrdquo CurrentPharmaceutical Design vol 15 no 28 pp 3209ndash3220 2009

[52] A Gaulton L J Bellis A P Bento et al et al ldquoA large-scalebioactivity database for drug discoveryrdquoNucleic Acids Researchpp D1100ndashD1107 2012

[53] W H Cropper Great Physicists The Life and Times of LeadingPhysicists from Galileo to Hawking Oxford University PressNew York NY USA 2004

[54] E L Bouzarth A Brooks R Camassa et al ldquoEpicyclic orbits ina viscous fluid about a precessing rod theory and experimentsat the micro-and macro-scalesrdquo Physical Review E vol 76 no1 part 2 Article ID 016313 2007

[55] M Laberge ldquoIntrinsic protein electric fields basic non-covalentinteractions and relationship to protein-induced Stark effectsrdquoBiochimica et BiophysicaActa vol 1386 no 2 pp 305ndash330 1998

[56] FM Fernandez and J AMorales ldquoPerturbation theorywithoutwave functions for the Zeeman effect in hydrogenrdquo PhysicalReview A vol 46 no 1 pp 318ndash326 1992

[57] B D Marshall and W G Chapman ldquoThermodynamic pertur-bation theory for associating fluids with small bond angleseffects of steric hindrance ring formation and double bondingrdquoPhysical Reiew E vol 87 no 5 Article ID 052307 12 pages 2013

[58] H Gonzalez-Diaz S Arrasate A Gomez-San Juan et al ldquoNewtheory for multiple input-output perturbations in complexmolecular systems 1 Linear QSPR electronegativity models inphysical organic and medicinal chemistryrdquo Current Topics inMedicinal Chemistry 2013

[59] H Gonzalez-Diaz S Arrasate N Sotomayor et al ldquoMIANNmodels in medicinal physical and organic chemistryrdquo CurrentTopics in Medicinal Chemistry vol 13 no 5 pp 619ndash641 2013

[60] G E P Box and GM Jenkins Time Series Analysis Forecastingand Control Holden-Day San Francisco Calif USA 1970

[61] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoUnified multi-target approach for the rational insilico design of anti-bladder cancer agentsrdquo Anti-Cancer Agentsin Medicial Chemistry vol 13 no 5 pp 791ndash800 2013

[62] A Speck-Planche V V Kleandrova and M N Cordeiro ldquoNewinsights toward the discovery of antibacterial agents multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugsrdquo TheEuropean Journal of Pharmaceutical Sciences vol 48 no 4-5pp 812ndash818 2013

[63] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoMulti-target inhibitors for proteins associated withAlzheimer in silico discovery using fragment-based descrip-torsrdquo Current Alzheimer Research vol 10 no 2 pp 117ndash1242013

[64] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoIn silico discovery and virtual screening of multi-target inhibitors for proteins in Mycobacterium tuberculosisrdquoCombinatorial Chemistry amp High Throughput Screening vol 15no 8 pp 666ndash673 2012

[65] A Speck-Planche V V Kleandrova F Luan and M N DS Cordeiro ldquoChemoinformatics in anti-cancer chemotherapymulti-target QSAR model for the in silico discovery of anti-breast cancer agentsrdquo The European Journal of PharmaceuticalSciences vol 47 no 1 pp 273ndash279 2012

Submit your manuscripts athttpwwwhindawicom

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Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Page 9: Research Article Model for Vaccine Design by …downloads.hindawi.com/journals/jir/2014/768515.pdfResearch Article Model for Vaccine Design by Prediction of B-Epitopes of IEDB Given

Journal of Immunology Research 9Ta

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

144652

GMRG

MKG

LVY

Hom

osapiens

Hom

osapiens

OAID

ELISA

144654

GPH

PTLE

VVPM

GRG

SMus

musculus

MD

AID

ELISAminus002minus0018minus0027minus0013minus002

104515

HDCR

PKKI

Mus

musculus

LaCrossevirus

AID

IFAIH

104521

IGTL

KKIL

DET

VKD

KIAKE

QRa

ttus

norvegicu

sStreptococcus

pyogenes

AID

ELISAminus004minus004minus0053minus004minus004

1

7367

CYGDWA

Hom

osapiens

Triticum

aestivum

OOA

ELISA

7374

CYGLP

DS

EPTK

TNGK

Mus

musculus

Tityus

serrulatus

AID

WIminus002minus0018minus0043minus0023minus0018

7367

CYGDWA

Hom

osapiens

Triticum

aestivum

OOA

ELISA

7374

CYGLP

DS

EPTK

TNGK

Mus

musculus

Tityus

serrulatus

AID

WIminus002minus0018minus0043minus0023minus0018

144610

DFF

TYK

Mus

musculus

Porcine

transmissible

AID

WI

144611

DFN

GSF

DMNGTITA

Oryctolagus

cuniculus

Escherich

iacoli

AID

ELISAminus001minus0007minus0018minus001minus0012

112047

AST

RESG

Hom

osapiens

Hom

osapiens

OAID

ELISA

112048

ATAST

MDHARH

GF

LPRH

RDT

Hom

osapiens

Hom

osapiens

AID

ELISA

005

005

005

0057

005

36136

LGGVFT

Hom

osapiens

Denguev

irus2

OOID

ELISA

36137

LGGWKL

QSD

PRAY

AL

Hom

osapiens

Ambrosia

artemisiifolia

OOA

RIA

001

001

0007

0013

000

9

115256

FREL

KDLK

GY

Hom

osapiens

Bostaurus

DEW

EDWI

115261

GDLE

ILL

QKW

ENG

ECAQ

KKI

Hom

osapiens

Bostaurus

OOA

FIAminus001minus001minus001

0minus000

9

129024

KADQLY

KHom

osapiens

Hom

osapiens

OAID

ELISA

129026

KAKK

PAAAAG

AKK

AKS

Oryctolagus

cuniculus

Hom

osapiens

AID

ELISA

003

0034

003

0037

003

148481

YTRD

LVYK

Rattu

snorvegicu

sHom

osapiens

AID

WI

148483

YVPIVT

FYSE

ISM

HSSRA

IPOryctolagus

cuniculus

MD

AID

ELISA

00002minus0007

0minus0002

150850

GY

Mus

musculus

Hum

anpapillomaviru

sAID

ELISA

150853

HIG

GLSI

LDPIFG

VL

Hom

osapiens

Dermatophagoides

farin

aeOOA

ACAb

B004

0038

004

0043

0039

107366

FPPK

PKD

Hom

osapiens

Hom

osapiens

OAID

ELISA

107376

GDRS

GYS

SPGSP

GMus

musculus

Hom

osapiens

AID

ACAb

Bminus003minus0028minus003minus0023minus0031

114859

ICGTD

GVTY

THom

osapiens

Gallusgallus

OOA

WI

114865

IVER

ETR

GQSE

NPL

WHALR

RRa

ttus

norvegicu

sHum

anherpesvirus

AID

ELISA

004

004

20035

0037

0038

62149

SVHLF

Hom

osapiens

MD

OAID

WI

62150

SVIALG

SQEG

ALH

QALA

GAI

Equu

scaballus

WestN

ilevirus

AID

IFAIHminus002minus0021minus0012minus0013minus0021

98455

SGSV

QFV

PIQ

Mus

musculus

Neisseria

meningitid

isAID

ELISA

98456

SICS

NNP

TCWAICK

RIPN

KKMus

musculus

Hum

anrespira

tory

virus

AID

IFAIH

004

004

0027

004

004

1

61783

STNKA

VVS

LSBo

staurus

Bovine

respira

tory

AID

ELISA

61791

STNPK

PQRK

TKRN

TNRR

PQHom

osapiens

HepatitisC

virus

EWEIR

ACAb

B004

0035

0029

0037

0039

100318

NAPK

TFQ

FIN

Mus

musculus

Bacillusa

nthracis

AID

ELISA

100319

NASSEL

HLL

GFG

INAEN

NHR

Hom

osapiens

Arachish

ypogaea

EWED

ELISA

0minus0002minus0012

00

10 Journal of Immunology ResearchTa

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

118947

PFSA

PPPA

Hom

osapiens

Hom

osapiens

OAID

ELISA

118948

PGAIEQG

PADDPG

EGPS

TGP

Hom

osapiens

Hum

anherpesvirus

NI

ACAb

Bminus004minus004minus004

1minus0036minus004

1

78323

YSFR

DMus

musculus

Bluetongue

virus1

AID

ELISA

78341

AALT

AEN

TAIK

KRN

ADAKA

Hom

osapiens

Streptococcus

mutan

sEE

EEL

ISA

001

0008

0007

0013

001

145831

IPLG

TRP

Mus

musculus

Hum

anpapillomaviru

sAID

ELISA

145841

KEDFR

YAISST

NEI

GLL

GA

Susscrofa

Classicalsw

ine

AID

PACminus004minus004minus0045minus004minus0043

119592

HTF

PAVLQ

Hom

osapiens

Hom

osapiens

OAID

ELISA

119596

IHIPSE

KIWRP

DLV

LYMus

musculus

Hom

osapiens

AID

RIA

001

0012

001

0017

000

9

58780

SKAANLSIIK

MD

Beetnecrotic

MD

WI

58783

SKAFSN

CYPY

DVP

DYA

SLOryctolagus

cuniculus

Influ

enza

Avirus

AID

RIAminus001minus000

4minus0014minus000

9minus0013

115153

MKG

VVC

TRIYEK

VHom

osapiens

Hom

osapiens

NI

ELISA

115155

NNQRK

KAKN

TPFN

MLK

RERN

Mus

musculus

Denguev

irus2

AID

ELISA

001

0012

000

40013

001

133629

LPLR

FOryctolagus

cuniculus

Gallusgallus

AID

ACAb

B133630

LPPG

LHV

FPLA

SNRS

Mus

musculus

MD

AID

SPRminus001minus0013minus0021minus001minus001

96215

EEEE

AE

DKE

DHom

osapiens

Hom

osapiens

OAID

ELISA

96216

EEEG

LLK

KSADTL

WNMQK

Mus

musculus

Mus

musculus

AID

ELISA

008

0082

0078

0087

008

39782

LTAASV

Hom

osapiens

Triticum

aestivum

OOA

ELISA

39788

LTAEL

KIYS

VIQ

AEI

NKH

LOryctolagus

cuniculus

Yersiniapestis

AID

ACAb

B001

0014minus0001

0007

000

9

107479

KFNWYV

DHom

osapiens

Hom

osapiens

OAID

ELISA

107482

KGEP

GL

PGRG

FPGFP

Mus

musculus

Hom

osapiens

AID

ACAb

B0

0002

00007minus0001

63569

TETV

NSD

IMacaca

mulatta

Shigellaflexneri

AID

ELISA

63573

TEVEL

KER

KHRIED

AVRN

AK

Hom

osapiens

Mycobacteriu

mlep

rae

OOID

ELISA

005

0036

004

10049

005

134028

DDTIS

Mus

musculus

Hom

osapiens

AID

NIAA

134029

DED

ENQS

PRSFQKK

TROryctolagus

cuniculus

Hom

osapiens

AID

ELISA

005

0053

005

005

004

8

23028

GVKY

AHom

osapiens

MD

OAID

WI

23032

GVLA

KDV

RFSQ

VHom

osapiens

MD

EWEIR

ACAb

B0

00

000

4minus0003

115293

IMCV

KKILDK

Hom

osapiens

Bostaurus

DEW

EDWI

115295

INPS

KEN

LCST

FCK

EVVRN

AHom

osapiens

Bostaurus

OOA

FIAminus003minus003minus003minus002minus0029

65105

TLTP

ENTL

Mus

musculus

Shigellaflexneri

AID

ELISA

65110

TLTS

GSD

LDRC

TTFD

DV

Oryctolagus

cuniculus

SARS

coronavirus

AID

ELISA

001

0013minus0003

001

001

1344

71PK

PEQ

Mus

musculus

Streptococcus

pneumoniae

AID

FACS

1344

72PL

LPGT

STTS

TGPC

KTHom

osapiens

HepatitisB

virus

AID

ELISA

002

0018

0019

002

0016

Journal of Immunology Research 11Ta

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

142228

LYCY

EQLN

DSSE

Hom

osapiens

Hum

anpapillomaviru

sNI

ELISA

142250

NWGDEP

SKRR

DRS

NSR

GRK

NFelis

catus

Felin

einfectio

usAID

ELISA

007

006

80071

0073

007

21549

GNYD

FWYQ

SHom

osapiens

Staphylococcus

aureus

OOID

ELISA

21553

GNYN

YKY

RYLR

HGK

LRPF

ERMus

musculus

SARS

coronavirus

AID

ELISA

003

0032

0021

0031

003

21549

GNYD

FWYQ

SHom

osapiens

Staphylococcus

aureus

OOID

ELISA

21553

GNYN

YKY

RYLR

HGK

LRPF

EROryctolagus

cuniculus

SARS

coronavirus

AID

ELISA

003

0034

0021

0031

003

34908

LAPL

GE

Hom

osapiens

HepatitisE

virus

EWEIR

ELISA

34914

LAPS

TLRS

LRKR

RLSSP

Hom

osapiens

Hum

anherpesvirus

OOID

ELISA

006

006

0059

0063

006

130454

CLFP

NNSY

CMus

musculus

MD

AID

NIAA

130456

CRPQ

VN

NPK

EWS

CAAC

Hom

osapiens

MD

OOD

ACAb

B001

0008

001

0011

0007

1964

4GFV

PSM

Hom

osapiens

Hepatitisd

elta

virus

OOID

ELISA

1964

7GFV

SASI

FGFQ

AEV

GPN

NTR

Oryctolagus

cuniculus

VacciniavirusW

RAID

ELISAminus001minus000

6minus0013minus000

9minus001

20678

GKRP

EMus

musculus

Streptococcus

pyogenes

AID

WI

20680

GKS

KRD

AKN

NAAK

LAVDKL

LMus

musculus

VacciniavirusW

RAID

WI

001

001

0001

001

001

123278

GYL

KDLP

TTOvisa

ries

Fasciolahepatica

AID

ELISA

123282

HAC

QKK

LLKF

EAL

QQEE

GEE

Rattu

snorvegicu

sGallusgallus

AID

PFFminus002minus0016minus0016minus002minus0025

141067

PLSLEP

DP

Mus

musculus

Hom

osapiens

AID

FACS

141073

REGVRW

RVMAIQ

Mus

musculus

Hom

osapiens

AID

ELISA

006

006

006

006

0056

139248

SFAG

TVIE

Mus

musculus

Classicalsw

ine

AID

WI

139305

TAAQ

ITQ

RKWEA

ARE

AEQ

RROryctolagus

cuniculus

Hom

osapiens

AID

IP003

0033

0026

003

003

104515

HDCR

PKKI

Mus

musculus

LaCrossevirus

AID

IFAIH

104520

IAKE

QE

NKE

TIGT

LKKI

LDE

Rattu

snorvegicu

sStreptococcus

pyogenes

AID

ELISAminus006minus006minus0073minus006minus0061

113517

HLY

ADGLT

DMus

musculus

Hum

anpapillomaviru

sAID

IFAIH

113518

HNKI

QA

IELE

DLL

RYS

KLYR

Mus

musculus

Hom

osapiens

AID

ELISA

002

002

0011

002

0019

156970

VER

HQ

Hom

osapiens

Hom

osapiens

OAID

ELISA

156975

WSSTV

LRVS

PTRT

VP

Mus

musculus

MD

AID

ELISA

001

0012

0003

0017

001

78252

PVQNLT

Mus

musculus

Porphyromonas

gingivalis

AID

WI

78253

QGGCG

RGWAFS

ATG

AIEA

Mus

musculus

Glycinem

axAID

ELISA

002

002

0017

002

0018

6068

CCPD

KNKS

Mus

musculus

Hum

anherpesvirus

AID

WI

6074

CCRH

KQKD

VGDVK

QTL

PPS

Ovisa

ries

MD

AID

ELISA

001

000

6000

4001

0008

12 Journal of Immunology Research

Table3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

53109

RAGVC

YHom

osapiens

Triticum

aestivum

OOA

ELISA

53116

RAILTA

FSPA

QDI

WGTS

Oryctolagus

cuniculus

SARS

coronavirus

AID

ELISAminus002minus0016minus0037minus0023minus002

147041

IPEQ

Hom

osapiens

Triticum

aestivum

OOA

WI

147064

KHQGA

QYV

WN

RTA

Hom

osapiens

Bostaurus

OOID

WI

006

006

0047

0057

006

7066

4VQ

VVYD

YQOryctolagus

cuniculus

Treponem

apallidu

mAID

ELISA

70667

VQWMN

RLIAFA

FAG

NHVS

PHom

osapiens

HepatitisC

virus

OOID

ELISA

005

004

6004

10049

005

134343

DLY

IKMus

musculus

Hum

anpapillomaviru

sAID

PhDIP

134344

DMAQ

VTV

GPG

LLGVS

TLMus

musculus

Hom

osapiens

AID

PhDIP

00minus000

90

0

Journal of Immunology Research 13

The first input term is the value 120582(120576119894119895)ref is the scoring

function 120582 of the efficiency of the initial process 120576119894119895(known

solution) The function 120582(120576119894119895)ref = 1 if the 119894th peptide could

experimentally be demonstrated to be a B-epitope in theassay of reference (reference) carried out in the conditions119888119895 120582(120576119894119895)ref = 0 otherwise The variational-perturbation

terms ΔΔ120594119888119895are at the same time terms typical of pertur-

bation theory and moving average (MA) functions used inBox-Jenkin models in time series [60] These new types ofterms account both for the deviation of the electronegativityof all amino acids in the sequence of the new peptidewith respect to the peptide of reference and with respectto all boundary conditions In Table 1 we give the overallclassification results obtainedwith thismodel Speck-Plancheet al [61ndash63] introduced different multitargetmultiplexingQSARmodels that incorporate this type of information basedon MAs The results obtained with the present model areexcellent comparedwith other similarmodels in the literatureuseful for other problems including moving average models[64 65] or perturbation models [58] Notably this is also thefirst model combining both perturbation theory and MAs ina QSPR context

The other input terms are the following The first Δ120594seq =120594(119898119902)ref minus120594(119898119894)new is the perturbation term for the variation

or in themean value of electronegativity for all amino acids inthe sequence of the peptide of reference The remnant inputvariables of the model ΔΔ120594

119888119895= Δ120594

119888119895-ref minus Δ120594119888119895-new =

[120594(119898119902)ref minus

lowast120594 (119888119902119903)ref] minus [120594(119898119894)new minus

lowast120594 (119888119894119895)new] quantify

values of the conditions of the new assay 119888119895-new that representperturbations with respect to the initial conditions 119888

119894119895-ref of

the assay of reference The quantities lowast120594(119888119894119895) and lowast120594(119888

119902119903) are

the average values of the mean electronegativity values 120594(119898119894)

and 120594(119898119902) for all new and reference peptides in IEDB that

are epitopes under the 119895th or 119903th boundary condition Thevalues of these terms have been tabulated for gt500 sourceorganisms gt50 host organisms gt10 biological process andgt30 experimental techniques We must substitute the valuesof 120594(119898

119894) and 120594(119898

119902) of the new and reference peptides and the

tabulated values of lowast120594(119888119894119895) and lowast120594(119888

119902119903) for all combinations

of boundary conditions to predict the perturbations of theaction as epitope of peptides In doing so we can found theoptimal sequence and boundary conditions towards the useof the peptide in the development of a vaccine In Table 2 wegive some of these values of lowast120594(119888

119894119895) and lowast120594(119888

119902119903)

In Table 3 we depict the sequences and input-outputboundary conditions for top perturbations present in IEDBAll these perturbations have observed value of 120582(120576

119894119895)new = 1

and predicted value also equal to 1 with a high probabilitySee Supplementary Material available online at httpdxdoiorg1011552014768515 file contains a full list of gt200000cases of perturbations

4 Conclusions

It is possible to develop generalmodels for vaccine design ableto predict the results of multiple input-output perturbations

in peptide sequence and experimental assay boundary condi-tions using ideas of QSPR analysis perturbation theory andBox and Jenkins MA operators The electronegativity valuescalculated withMARCH-INSIDE seem to be goodmoleculardescriptors for this type of QSPR-perturbation models

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

The present study was partially supported by GrantsAGL2010-22290-C02 and AGL2011-30563-C03 from Minis-terio de Ciencia e Innovacion Spain and Grant CN 2012155from Xunta de Galicia Spain

References

[1] B Peters J Sidney P Bourne et al ldquoThe design and implemen-tation of the immune epitope database and analysis resourcerdquoImmunogenetics vol 57 no 5 pp 326ndash336 2005

[2] B Peters J Sidney P Bourne et al ldquoThe immune epitopedatabase and analysis resource from vision to blueprintrdquo PLoSBiology vol 3 no 3 p e91 2005

[3] PWang A AMorgan Q Zhang A Sette and B Peters ldquoAuto-mating document classification for the Immune Epitope Data-baserdquo BMC Bioinformatics vol 8 article 269 2007

[4] A Sette ldquoThe immune epitope database and analysis resourcefrom vision to blueprintrdquo Genome Informatics vol 15 no 2 p299 2004

[5] N Salimi W Fleri B Peters and A Sette ldquoThe immune epi-tope database a historical retrospective of the first decaderdquoImmunology vol 137 no 2 pp 117ndash123 2012

[6] Y Kim A Sette and B Peters ldquoApplications for T-cell epitopequeries and tools in the Immune EpitopeDatabase andAnalysisResourcerdquo Journal of Immunological Methods vol 374 no 1-2pp 62ndash69 2011

[7] Y Kim J Ponomarenko Z Zhu D Tamang et al ldquoImmuneepitope database analysis resourcerdquo Nucleic Acids Research ppW525ndashW530 2012

[8] A M Helguera R D Combes M P Gonzalez and M N DS Cordeiro ldquoApplications of 2D descriptors in drug design aDRAGON talerdquo Current Topics in Medicinal Chemistry vol 8no 18 pp 1628ndash1655 2008

[9] GM Casanola-Martın Y Marrero-Ponce M T H Khan et alldquoDragon method for finding novel tyrosinase inhibitors Biosil-ico identification and experimental in vitro assaysrdquo The Euro-pean Journal of Medicinal Chemistry vol 42 no 11-12 pp 1370ndash1381 2007

[10] I V Tetko J Gasteiger R Todeschini et al ldquoVirtual computa-tional chemistry laboratorymdashdesign and descriptionrdquo Journalof Computer-AidedMolecular Design vol 19 no 6 pp 453ndash4632005

[11] A R Katritzky A Oliferenko A Lomaka and M KarelsonldquoSix-membered cyclic ureas as HIV-1 protease inhibitors aQSAR study based on CODESSA PRO approachrdquo Bioorganicand Medicinal Chemistry Letters vol 12 no 23 pp 3453ndash34572002

14 Journal of Immunology Research

[12] A R Katritzky S Perumal R Petrukhin and E Klein-peter ldquoCODESSA-based theoretical QSPRmodel for hydantoinHPLC-RT lipophilicitiesrdquo Journal of Chemical Information andComputer Sciences vol 41 no 3 pp 569ndash574 2001

[13] S Vilar G Cozza and S Moro ldquoMedicinal chemistry and themolecular operating environment (MOE) application of QSARand molecular docking to drug discoveryrdquo Current Topics inMedicinal Chemistry vol 8 no 18 pp 1555ndash1572 2008

[14] G Marzaro A Chilin A Guiotto et al ldquoUsing the TOPS-MODE approach to fit multi-target QSAR models for tyrosinekinases inhibitorsrdquo The European Journal of Medicinal Chem-istry vol 46 no 6 pp 2185ndash2192 2011

[15] S Vilar E Estrada E Uriarte L Santana and Y Gutierrez ldquoInsilico studies toward the discovery of new anti-HIV nucleosidecompounds through the use of tops-mode and 2D3D con-nectivity indices 2 Purine derivativesrdquo Journal of ChemicalInformation and Modeling vol 45 no 2 pp 502ndash514 2005

[16] E Estrada J A Quincoces and G Patlewicz ldquoCreating molec-ular diversity from antioxidants in Brazilian propolis combina-tion of TOPS-MODE QSAR and virtual structure generationrdquoMolecular Diversity vol 8 no 1 pp 21ndash33 2004

[17] E Estrada andHGonzalez ldquoWhat are the limits of applicabilityfor graph theoretic descriptors in QSPRQSAR modelingdipole moments of aromatic compounds with TOPS-MODEdescriptorsrdquo Journal of Chemical Information and ComputerSciences vol 43 no 1 pp 75ndash84 2003

[18] Y Marrero-Ponce J A Castillo-Garit E Olazabal et al ldquoTom-ocomd-Cardd a novel approach for computer-aided ldquorationalrdquodrug design I Theoretical and experimental assessment of apromising method for computational screening and in silicodesign of new anthelmintic compoundsrdquo Journal of Computer-Aided Molecular Design vol 18 no 10 pp 615ndash634 2004

[19] Y Marrero Ponce R Medina Marrero E A Castro et al ldquoPro-tein quadratic indices of the ldquomacromolecular pseudographrsquos 120572-carbon atom adjacency matrixrdquo 1 prediction of arc repressoralanine-mutantrsquos stabilityrdquoMolecules vol 9 no 12 pp 1124ndash11472004

[20] H Gonzalez-Dıaz F Prado-Prado and F M Ubeira ldquoPredict-ing antimicrobial drugs and targets with the MARCH-INSIDEapproachrdquo Current Topics in Medicinal Chemistry vol 8 no 18pp 1676ndash1690 2008

[21] R Garcıa-Domenech J Galvez J V de Julian-Ortiz and LPogliani ldquoSomenew trends in chemical graph theoryrdquoChemicalReviews vol 108 no 3 pp 1127ndash1169 2008

[22] E Estrada and E Uriarte ldquoRecent advances on the role of topo-logical indices in drug discovery researchrdquo Current MedicinalChemistry vol 8 no 13 pp 1573ndash1588 2001

[23] R Todeschini andVConsonniHandbook ofMolecularDescrip-tors Wiley-VCH Weinheim Germany 2008

[24] E Estrada E J Delgado J B Alderete and G A Jana ldquoQuan-tum-connectivity descriptors inmodeling solubility of environ-mentally important organic compoundsrdquo Journal of Computa-tional Chemistry vol 25 no 14 pp 1787ndash1796 2004

[25] E Besalu X Girones L Amat andR Carbo-Dorca ldquoMolecularquantum similarity and the fundamentals of QSARrdquo Accountsof Chemical Research vol 35 no 5 pp 289ndash295 2002

[26] D A Rincon M N D S Cordeiro and R A Mosquera ldquoOnthe electronic structure of cocaine and its metabolitesrdquo Journalof Physical Chemistry A vol 113 no 50 pp 13937ndash13942 2009

[27] M Mandado M J Gonzalez-Moa and R A MosqueraldquoChemical graph theory and n-center electron delocalization

indices a study on polycyclic aromatic hydrocarbonsrdquo Journalof Computational Chemistry vol 28 no 10 pp 1625ndash1633 2007

[28] THill andP Lewicki STATISTICSMethods andApplications AComprehensive Reference for Science Industry and DataMiningStatSoft Tulsa Okla USA 2006

[29] E Frank M Hall L Trigg G Holmes and I H Witten ldquoDatamining in bioinformatics using Wekardquo Bioinformatics vol 20no 15 pp 2479ndash2481 2004

[30] G Patlewicz N Ball E D Booth E Hulzebos E Zvinavasheand C Hennes ldquoUse of category approaches read-across and(Q)SAR general considerationsrdquo Regulatory Toxicology andPharmacology vol 67 no 1 pp 1ndash12 2013

[31] D W Roberts and G Y Patlewicz ldquoUpdating the skin sensiti-zation in vitro data assessment paradigm in 2009mdasha chemistryand QSAR perspectiverdquo Journal of Applied Toxicology vol 30no 3 pp 286ndash288 2010

[32] D W Roberts and G Y Patlewicz ldquoNonanimal alternatives forskin sensitization letter to the editorrdquoToxicological Sciences vol106 no 2 pp 572ndash574 2008

[33] E Estrada G Patlewicz and Y Gutierrez ldquoFrom knowledgegeneration to knowledge archive a general strategy usingTOPS-MODE with DEREK to formulate new alerts for skinsensitizationrdquo Journal of Chemical Information and ComputerSciences vol 44 no 2 pp 688ndash698 2004

[34] G F Gerberick C A Ryan P S Kern et al ldquoA chemical datasetfor evaluation of alternative approaches to skin-sensitizationtestingrdquo Contact Dermatitis vol 50 no 5 pp 274ndash288 2004

[35] G Y Patlewicz D A Basketter C K Smith Pease et al ldquoFurtherevaluation of quantitative structurendashactivity relationship mod-els for the prediction of the skin sensitization potency ofselected fragrance allergensrdquo Contact Dermatitis vol 50 no 2pp 91ndash97 2004

[36] E Estrada G Patlewicz M Chamberlain D Basketter andS Larbey ldquoComputer-aided knowledge generation for under-standing skin sensitization mechanisms the TOPS-MODEapproachrdquo Chemical Research in Toxicology vol 16 no 10 pp1226ndash1235 2003

[37] G Patlewicz R Rodford and J D Walker ldquoQuantitative struc-ture-activity relationships for predicting skin and eye irritationrdquoEnvironmental Toxicology and Chemistry vol 22 no 8 pp1862ndash1869 2003

[38] G Y Patlewicz R A Rodford G Ellis and M D Barratt ldquoAQSAR model for the eye irritation of cationic surfactantsrdquo Tox-icology In Vitro vol 14 no 1 pp 79ndash84 2000

[39] E Tenorio-Borroto C G Penuelas Rivas J C VasquezChagoyan N Castanedo F J Prado-Prado and X Garcia-Mera ldquoANN multiplexing model of drugs effect on macro-phages theoretical and flow cytometry study on the cyto-toxicity of the anti-microbial drug G1 in spleenrdquo Bioorganic ampMedicinal Chemistry vol 20 no 20 pp 6181ndash6194 2012

[40] D R Flower H McSparron M J Blythe et al ldquoComputationalvaccinology quantitative approachesrdquo Novartis FoundationSymposium vol 254 pp 102ndash120 2003

[41] I A Doytchinova P Guan and D R Flower ldquoQuantitativestructure-activity relationships and the prediction of MHCsupermotifsrdquoMethods vol 34 no 4 pp 444ndash453 2004

[42] Y Xiao and M R Segal ldquoPrediction of genomewide conservedepitope profiles of HIV-1 classifier choice and peptide rep-resentationrdquo Statistical Applications in Genetics and MolecularBiology vol 4 no 1 article 25 2005

Journal of Immunology Research 15

[43] T Fagerberg V Zoete S Viatte P Baumgaertner P M Alves PRomero et al ldquoPrediction of cross-recognition of peptide-HLAA2 by melan-a-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationshipsrdquo PLoSONE vol 8 no 7 Article ID e65590 2013

[44] D Barh A N Misra A Kumar and A Vasco ldquoA novel strategyof epitope design inNeisseria gonorrhoeaerdquo Bioinformation vol5 no 2 pp 77ndash82 2010

[45] J Bi R Song H Yang et al ldquoStepwise identification of HLA-Alowast0201-restricted CD8+ T-cell epitope peptides from herpessimplex virus type 1 genome boosted by a steprank schemerdquoBiopolymers vol 96 no 3 pp 328ndash339 2011

[46] R D Bremel and E J Homan ldquoAn integrated approach to epit-ope analysis II a system for proteomic-scale prediction ofimmunological characteristicsrdquo Immunome Research vol 6 no1 article 8 2010

[47] C M Diez-Rivero B Chenlo P Zuluaga and P A RecheldquoQuantitative modeling of peptide binding to TAP using sup-port vector machinerdquo Proteins vol 78 no 1 pp 63ndash72 2010

[48] E Martınez-Naves E M Lafuente and P A Reche ldquoRecogni-tion of the ligand-type specificity of classical and non-classicalMHC I proteinsrdquo FEBS Letters vol 585 no 21 pp 3478ndash34842011

[49] M Bhasin E L Reinherz and P A Reche ldquoRecognition andclassification of histones using support vectormachinerdquo Journalof Computational Biology vol 13 no 1 pp 102ndash112 2006

[50] P A Reche and E L Reinherz ldquoPEPVAC a web server formulti-epitope vaccine development based on the prediction ofsupertypic MHC ligandsrdquo Nucleic Acids Research vol 33 no 2pp W138ndashW142 2005

[51] E M Lafuente and P A Reche ldquoPrediction of MHC-peptidebinding a systematic and comprehensive overviewrdquo CurrentPharmaceutical Design vol 15 no 28 pp 3209ndash3220 2009

[52] A Gaulton L J Bellis A P Bento et al et al ldquoA large-scalebioactivity database for drug discoveryrdquoNucleic Acids Researchpp D1100ndashD1107 2012

[53] W H Cropper Great Physicists The Life and Times of LeadingPhysicists from Galileo to Hawking Oxford University PressNew York NY USA 2004

[54] E L Bouzarth A Brooks R Camassa et al ldquoEpicyclic orbits ina viscous fluid about a precessing rod theory and experimentsat the micro-and macro-scalesrdquo Physical Review E vol 76 no1 part 2 Article ID 016313 2007

[55] M Laberge ldquoIntrinsic protein electric fields basic non-covalentinteractions and relationship to protein-induced Stark effectsrdquoBiochimica et BiophysicaActa vol 1386 no 2 pp 305ndash330 1998

[56] FM Fernandez and J AMorales ldquoPerturbation theorywithoutwave functions for the Zeeman effect in hydrogenrdquo PhysicalReview A vol 46 no 1 pp 318ndash326 1992

[57] B D Marshall and W G Chapman ldquoThermodynamic pertur-bation theory for associating fluids with small bond angleseffects of steric hindrance ring formation and double bondingrdquoPhysical Reiew E vol 87 no 5 Article ID 052307 12 pages 2013

[58] H Gonzalez-Diaz S Arrasate A Gomez-San Juan et al ldquoNewtheory for multiple input-output perturbations in complexmolecular systems 1 Linear QSPR electronegativity models inphysical organic and medicinal chemistryrdquo Current Topics inMedicinal Chemistry 2013

[59] H Gonzalez-Diaz S Arrasate N Sotomayor et al ldquoMIANNmodels in medicinal physical and organic chemistryrdquo CurrentTopics in Medicinal Chemistry vol 13 no 5 pp 619ndash641 2013

[60] G E P Box and GM Jenkins Time Series Analysis Forecastingand Control Holden-Day San Francisco Calif USA 1970

[61] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoUnified multi-target approach for the rational insilico design of anti-bladder cancer agentsrdquo Anti-Cancer Agentsin Medicial Chemistry vol 13 no 5 pp 791ndash800 2013

[62] A Speck-Planche V V Kleandrova and M N Cordeiro ldquoNewinsights toward the discovery of antibacterial agents multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugsrdquo TheEuropean Journal of Pharmaceutical Sciences vol 48 no 4-5pp 812ndash818 2013

[63] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoMulti-target inhibitors for proteins associated withAlzheimer in silico discovery using fragment-based descrip-torsrdquo Current Alzheimer Research vol 10 no 2 pp 117ndash1242013

[64] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoIn silico discovery and virtual screening of multi-target inhibitors for proteins in Mycobacterium tuberculosisrdquoCombinatorial Chemistry amp High Throughput Screening vol 15no 8 pp 666ndash673 2012

[65] A Speck-Planche V V Kleandrova F Luan and M N DS Cordeiro ldquoChemoinformatics in anti-cancer chemotherapymulti-target QSAR model for the in silico discovery of anti-breast cancer agentsrdquo The European Journal of PharmaceuticalSciences vol 47 no 1 pp 273ndash279 2012

Submit your manuscripts athttpwwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MEDIATORSINFLAMMATION

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Behavioural Neurology

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Disease Markers

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OncologyJournal of

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Oxidative Medicine and Cellular Longevity

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The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Computational and Mathematical Methods in Medicine

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Research and TreatmentAIDS

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Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Page 10: Research Article Model for Vaccine Design by …downloads.hindawi.com/journals/jir/2014/768515.pdfResearch Article Model for Vaccine Design by Prediction of B-Epitopes of IEDB Given

10 Journal of Immunology ResearchTa

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

118947

PFSA

PPPA

Hom

osapiens

Hom

osapiens

OAID

ELISA

118948

PGAIEQG

PADDPG

EGPS

TGP

Hom

osapiens

Hum

anherpesvirus

NI

ACAb

Bminus004minus004minus004

1minus0036minus004

1

78323

YSFR

DMus

musculus

Bluetongue

virus1

AID

ELISA

78341

AALT

AEN

TAIK

KRN

ADAKA

Hom

osapiens

Streptococcus

mutan

sEE

EEL

ISA

001

0008

0007

0013

001

145831

IPLG

TRP

Mus

musculus

Hum

anpapillomaviru

sAID

ELISA

145841

KEDFR

YAISST

NEI

GLL

GA

Susscrofa

Classicalsw

ine

AID

PACminus004minus004minus0045minus004minus0043

119592

HTF

PAVLQ

Hom

osapiens

Hom

osapiens

OAID

ELISA

119596

IHIPSE

KIWRP

DLV

LYMus

musculus

Hom

osapiens

AID

RIA

001

0012

001

0017

000

9

58780

SKAANLSIIK

MD

Beetnecrotic

MD

WI

58783

SKAFSN

CYPY

DVP

DYA

SLOryctolagus

cuniculus

Influ

enza

Avirus

AID

RIAminus001minus000

4minus0014minus000

9minus0013

115153

MKG

VVC

TRIYEK

VHom

osapiens

Hom

osapiens

NI

ELISA

115155

NNQRK

KAKN

TPFN

MLK

RERN

Mus

musculus

Denguev

irus2

AID

ELISA

001

0012

000

40013

001

133629

LPLR

FOryctolagus

cuniculus

Gallusgallus

AID

ACAb

B133630

LPPG

LHV

FPLA

SNRS

Mus

musculus

MD

AID

SPRminus001minus0013minus0021minus001minus001

96215

EEEE

AE

DKE

DHom

osapiens

Hom

osapiens

OAID

ELISA

96216

EEEG

LLK

KSADTL

WNMQK

Mus

musculus

Mus

musculus

AID

ELISA

008

0082

0078

0087

008

39782

LTAASV

Hom

osapiens

Triticum

aestivum

OOA

ELISA

39788

LTAEL

KIYS

VIQ

AEI

NKH

LOryctolagus

cuniculus

Yersiniapestis

AID

ACAb

B001

0014minus0001

0007

000

9

107479

KFNWYV

DHom

osapiens

Hom

osapiens

OAID

ELISA

107482

KGEP

GL

PGRG

FPGFP

Mus

musculus

Hom

osapiens

AID

ACAb

B0

0002

00007minus0001

63569

TETV

NSD

IMacaca

mulatta

Shigellaflexneri

AID

ELISA

63573

TEVEL

KER

KHRIED

AVRN

AK

Hom

osapiens

Mycobacteriu

mlep

rae

OOID

ELISA

005

0036

004

10049

005

134028

DDTIS

Mus

musculus

Hom

osapiens

AID

NIAA

134029

DED

ENQS

PRSFQKK

TROryctolagus

cuniculus

Hom

osapiens

AID

ELISA

005

0053

005

005

004

8

23028

GVKY

AHom

osapiens

MD

OAID

WI

23032

GVLA

KDV

RFSQ

VHom

osapiens

MD

EWEIR

ACAb

B0

00

000

4minus0003

115293

IMCV

KKILDK

Hom

osapiens

Bostaurus

DEW

EDWI

115295

INPS

KEN

LCST

FCK

EVVRN

AHom

osapiens

Bostaurus

OOA

FIAminus003minus003minus003minus002minus0029

65105

TLTP

ENTL

Mus

musculus

Shigellaflexneri

AID

ELISA

65110

TLTS

GSD

LDRC

TTFD

DV

Oryctolagus

cuniculus

SARS

coronavirus

AID

ELISA

001

0013minus0003

001

001

1344

71PK

PEQ

Mus

musculus

Streptococcus

pneumoniae

AID

FACS

1344

72PL

LPGT

STTS

TGPC

KTHom

osapiens

HepatitisB

virus

AID

ELISA

002

0018

0019

002

0016

Journal of Immunology Research 11Ta

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

142228

LYCY

EQLN

DSSE

Hom

osapiens

Hum

anpapillomaviru

sNI

ELISA

142250

NWGDEP

SKRR

DRS

NSR

GRK

NFelis

catus

Felin

einfectio

usAID

ELISA

007

006

80071

0073

007

21549

GNYD

FWYQ

SHom

osapiens

Staphylococcus

aureus

OOID

ELISA

21553

GNYN

YKY

RYLR

HGK

LRPF

ERMus

musculus

SARS

coronavirus

AID

ELISA

003

0032

0021

0031

003

21549

GNYD

FWYQ

SHom

osapiens

Staphylococcus

aureus

OOID

ELISA

21553

GNYN

YKY

RYLR

HGK

LRPF

EROryctolagus

cuniculus

SARS

coronavirus

AID

ELISA

003

0034

0021

0031

003

34908

LAPL

GE

Hom

osapiens

HepatitisE

virus

EWEIR

ELISA

34914

LAPS

TLRS

LRKR

RLSSP

Hom

osapiens

Hum

anherpesvirus

OOID

ELISA

006

006

0059

0063

006

130454

CLFP

NNSY

CMus

musculus

MD

AID

NIAA

130456

CRPQ

VN

NPK

EWS

CAAC

Hom

osapiens

MD

OOD

ACAb

B001

0008

001

0011

0007

1964

4GFV

PSM

Hom

osapiens

Hepatitisd

elta

virus

OOID

ELISA

1964

7GFV

SASI

FGFQ

AEV

GPN

NTR

Oryctolagus

cuniculus

VacciniavirusW

RAID

ELISAminus001minus000

6minus0013minus000

9minus001

20678

GKRP

EMus

musculus

Streptococcus

pyogenes

AID

WI

20680

GKS

KRD

AKN

NAAK

LAVDKL

LMus

musculus

VacciniavirusW

RAID

WI

001

001

0001

001

001

123278

GYL

KDLP

TTOvisa

ries

Fasciolahepatica

AID

ELISA

123282

HAC

QKK

LLKF

EAL

QQEE

GEE

Rattu

snorvegicu

sGallusgallus

AID

PFFminus002minus0016minus0016minus002minus0025

141067

PLSLEP

DP

Mus

musculus

Hom

osapiens

AID

FACS

141073

REGVRW

RVMAIQ

Mus

musculus

Hom

osapiens

AID

ELISA

006

006

006

006

0056

139248

SFAG

TVIE

Mus

musculus

Classicalsw

ine

AID

WI

139305

TAAQ

ITQ

RKWEA

ARE

AEQ

RROryctolagus

cuniculus

Hom

osapiens

AID

IP003

0033

0026

003

003

104515

HDCR

PKKI

Mus

musculus

LaCrossevirus

AID

IFAIH

104520

IAKE

QE

NKE

TIGT

LKKI

LDE

Rattu

snorvegicu

sStreptococcus

pyogenes

AID

ELISAminus006minus006minus0073minus006minus0061

113517

HLY

ADGLT

DMus

musculus

Hum

anpapillomaviru

sAID

IFAIH

113518

HNKI

QA

IELE

DLL

RYS

KLYR

Mus

musculus

Hom

osapiens

AID

ELISA

002

002

0011

002

0019

156970

VER

HQ

Hom

osapiens

Hom

osapiens

OAID

ELISA

156975

WSSTV

LRVS

PTRT

VP

Mus

musculus

MD

AID

ELISA

001

0012

0003

0017

001

78252

PVQNLT

Mus

musculus

Porphyromonas

gingivalis

AID

WI

78253

QGGCG

RGWAFS

ATG

AIEA

Mus

musculus

Glycinem

axAID

ELISA

002

002

0017

002

0018

6068

CCPD

KNKS

Mus

musculus

Hum

anherpesvirus

AID

WI

6074

CCRH

KQKD

VGDVK

QTL

PPS

Ovisa

ries

MD

AID

ELISA

001

000

6000

4001

0008

12 Journal of Immunology Research

Table3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

53109

RAGVC

YHom

osapiens

Triticum

aestivum

OOA

ELISA

53116

RAILTA

FSPA

QDI

WGTS

Oryctolagus

cuniculus

SARS

coronavirus

AID

ELISAminus002minus0016minus0037minus0023minus002

147041

IPEQ

Hom

osapiens

Triticum

aestivum

OOA

WI

147064

KHQGA

QYV

WN

RTA

Hom

osapiens

Bostaurus

OOID

WI

006

006

0047

0057

006

7066

4VQ

VVYD

YQOryctolagus

cuniculus

Treponem

apallidu

mAID

ELISA

70667

VQWMN

RLIAFA

FAG

NHVS

PHom

osapiens

HepatitisC

virus

OOID

ELISA

005

004

6004

10049

005

134343

DLY

IKMus

musculus

Hum

anpapillomaviru

sAID

PhDIP

134344

DMAQ

VTV

GPG

LLGVS

TLMus

musculus

Hom

osapiens

AID

PhDIP

00minus000

90

0

Journal of Immunology Research 13

The first input term is the value 120582(120576119894119895)ref is the scoring

function 120582 of the efficiency of the initial process 120576119894119895(known

solution) The function 120582(120576119894119895)ref = 1 if the 119894th peptide could

experimentally be demonstrated to be a B-epitope in theassay of reference (reference) carried out in the conditions119888119895 120582(120576119894119895)ref = 0 otherwise The variational-perturbation

terms ΔΔ120594119888119895are at the same time terms typical of pertur-

bation theory and moving average (MA) functions used inBox-Jenkin models in time series [60] These new types ofterms account both for the deviation of the electronegativityof all amino acids in the sequence of the new peptidewith respect to the peptide of reference and with respectto all boundary conditions In Table 1 we give the overallclassification results obtainedwith thismodel Speck-Plancheet al [61ndash63] introduced different multitargetmultiplexingQSARmodels that incorporate this type of information basedon MAs The results obtained with the present model areexcellent comparedwith other similarmodels in the literatureuseful for other problems including moving average models[64 65] or perturbation models [58] Notably this is also thefirst model combining both perturbation theory and MAs ina QSPR context

The other input terms are the following The first Δ120594seq =120594(119898119902)ref minus120594(119898119894)new is the perturbation term for the variation

or in themean value of electronegativity for all amino acids inthe sequence of the peptide of reference The remnant inputvariables of the model ΔΔ120594

119888119895= Δ120594

119888119895-ref minus Δ120594119888119895-new =

[120594(119898119902)ref minus

lowast120594 (119888119902119903)ref] minus [120594(119898119894)new minus

lowast120594 (119888119894119895)new] quantify

values of the conditions of the new assay 119888119895-new that representperturbations with respect to the initial conditions 119888

119894119895-ref of

the assay of reference The quantities lowast120594(119888119894119895) and lowast120594(119888

119902119903) are

the average values of the mean electronegativity values 120594(119898119894)

and 120594(119898119902) for all new and reference peptides in IEDB that

are epitopes under the 119895th or 119903th boundary condition Thevalues of these terms have been tabulated for gt500 sourceorganisms gt50 host organisms gt10 biological process andgt30 experimental techniques We must substitute the valuesof 120594(119898

119894) and 120594(119898

119902) of the new and reference peptides and the

tabulated values of lowast120594(119888119894119895) and lowast120594(119888

119902119903) for all combinations

of boundary conditions to predict the perturbations of theaction as epitope of peptides In doing so we can found theoptimal sequence and boundary conditions towards the useof the peptide in the development of a vaccine In Table 2 wegive some of these values of lowast120594(119888

119894119895) and lowast120594(119888

119902119903)

In Table 3 we depict the sequences and input-outputboundary conditions for top perturbations present in IEDBAll these perturbations have observed value of 120582(120576

119894119895)new = 1

and predicted value also equal to 1 with a high probabilitySee Supplementary Material available online at httpdxdoiorg1011552014768515 file contains a full list of gt200000cases of perturbations

4 Conclusions

It is possible to develop generalmodels for vaccine design ableto predict the results of multiple input-output perturbations

in peptide sequence and experimental assay boundary condi-tions using ideas of QSPR analysis perturbation theory andBox and Jenkins MA operators The electronegativity valuescalculated withMARCH-INSIDE seem to be goodmoleculardescriptors for this type of QSPR-perturbation models

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

The present study was partially supported by GrantsAGL2010-22290-C02 and AGL2011-30563-C03 from Minis-terio de Ciencia e Innovacion Spain and Grant CN 2012155from Xunta de Galicia Spain

References

[1] B Peters J Sidney P Bourne et al ldquoThe design and implemen-tation of the immune epitope database and analysis resourcerdquoImmunogenetics vol 57 no 5 pp 326ndash336 2005

[2] B Peters J Sidney P Bourne et al ldquoThe immune epitopedatabase and analysis resource from vision to blueprintrdquo PLoSBiology vol 3 no 3 p e91 2005

[3] PWang A AMorgan Q Zhang A Sette and B Peters ldquoAuto-mating document classification for the Immune Epitope Data-baserdquo BMC Bioinformatics vol 8 article 269 2007

[4] A Sette ldquoThe immune epitope database and analysis resourcefrom vision to blueprintrdquo Genome Informatics vol 15 no 2 p299 2004

[5] N Salimi W Fleri B Peters and A Sette ldquoThe immune epi-tope database a historical retrospective of the first decaderdquoImmunology vol 137 no 2 pp 117ndash123 2012

[6] Y Kim A Sette and B Peters ldquoApplications for T-cell epitopequeries and tools in the Immune EpitopeDatabase andAnalysisResourcerdquo Journal of Immunological Methods vol 374 no 1-2pp 62ndash69 2011

[7] Y Kim J Ponomarenko Z Zhu D Tamang et al ldquoImmuneepitope database analysis resourcerdquo Nucleic Acids Research ppW525ndashW530 2012

[8] A M Helguera R D Combes M P Gonzalez and M N DS Cordeiro ldquoApplications of 2D descriptors in drug design aDRAGON talerdquo Current Topics in Medicinal Chemistry vol 8no 18 pp 1628ndash1655 2008

[9] GM Casanola-Martın Y Marrero-Ponce M T H Khan et alldquoDragon method for finding novel tyrosinase inhibitors Biosil-ico identification and experimental in vitro assaysrdquo The Euro-pean Journal of Medicinal Chemistry vol 42 no 11-12 pp 1370ndash1381 2007

[10] I V Tetko J Gasteiger R Todeschini et al ldquoVirtual computa-tional chemistry laboratorymdashdesign and descriptionrdquo Journalof Computer-AidedMolecular Design vol 19 no 6 pp 453ndash4632005

[11] A R Katritzky A Oliferenko A Lomaka and M KarelsonldquoSix-membered cyclic ureas as HIV-1 protease inhibitors aQSAR study based on CODESSA PRO approachrdquo Bioorganicand Medicinal Chemistry Letters vol 12 no 23 pp 3453ndash34572002

14 Journal of Immunology Research

[12] A R Katritzky S Perumal R Petrukhin and E Klein-peter ldquoCODESSA-based theoretical QSPRmodel for hydantoinHPLC-RT lipophilicitiesrdquo Journal of Chemical Information andComputer Sciences vol 41 no 3 pp 569ndash574 2001

[13] S Vilar G Cozza and S Moro ldquoMedicinal chemistry and themolecular operating environment (MOE) application of QSARand molecular docking to drug discoveryrdquo Current Topics inMedicinal Chemistry vol 8 no 18 pp 1555ndash1572 2008

[14] G Marzaro A Chilin A Guiotto et al ldquoUsing the TOPS-MODE approach to fit multi-target QSAR models for tyrosinekinases inhibitorsrdquo The European Journal of Medicinal Chem-istry vol 46 no 6 pp 2185ndash2192 2011

[15] S Vilar E Estrada E Uriarte L Santana and Y Gutierrez ldquoInsilico studies toward the discovery of new anti-HIV nucleosidecompounds through the use of tops-mode and 2D3D con-nectivity indices 2 Purine derivativesrdquo Journal of ChemicalInformation and Modeling vol 45 no 2 pp 502ndash514 2005

[16] E Estrada J A Quincoces and G Patlewicz ldquoCreating molec-ular diversity from antioxidants in Brazilian propolis combina-tion of TOPS-MODE QSAR and virtual structure generationrdquoMolecular Diversity vol 8 no 1 pp 21ndash33 2004

[17] E Estrada andHGonzalez ldquoWhat are the limits of applicabilityfor graph theoretic descriptors in QSPRQSAR modelingdipole moments of aromatic compounds with TOPS-MODEdescriptorsrdquo Journal of Chemical Information and ComputerSciences vol 43 no 1 pp 75ndash84 2003

[18] Y Marrero-Ponce J A Castillo-Garit E Olazabal et al ldquoTom-ocomd-Cardd a novel approach for computer-aided ldquorationalrdquodrug design I Theoretical and experimental assessment of apromising method for computational screening and in silicodesign of new anthelmintic compoundsrdquo Journal of Computer-Aided Molecular Design vol 18 no 10 pp 615ndash634 2004

[19] Y Marrero Ponce R Medina Marrero E A Castro et al ldquoPro-tein quadratic indices of the ldquomacromolecular pseudographrsquos 120572-carbon atom adjacency matrixrdquo 1 prediction of arc repressoralanine-mutantrsquos stabilityrdquoMolecules vol 9 no 12 pp 1124ndash11472004

[20] H Gonzalez-Dıaz F Prado-Prado and F M Ubeira ldquoPredict-ing antimicrobial drugs and targets with the MARCH-INSIDEapproachrdquo Current Topics in Medicinal Chemistry vol 8 no 18pp 1676ndash1690 2008

[21] R Garcıa-Domenech J Galvez J V de Julian-Ortiz and LPogliani ldquoSomenew trends in chemical graph theoryrdquoChemicalReviews vol 108 no 3 pp 1127ndash1169 2008

[22] E Estrada and E Uriarte ldquoRecent advances on the role of topo-logical indices in drug discovery researchrdquo Current MedicinalChemistry vol 8 no 13 pp 1573ndash1588 2001

[23] R Todeschini andVConsonniHandbook ofMolecularDescrip-tors Wiley-VCH Weinheim Germany 2008

[24] E Estrada E J Delgado J B Alderete and G A Jana ldquoQuan-tum-connectivity descriptors inmodeling solubility of environ-mentally important organic compoundsrdquo Journal of Computa-tional Chemistry vol 25 no 14 pp 1787ndash1796 2004

[25] E Besalu X Girones L Amat andR Carbo-Dorca ldquoMolecularquantum similarity and the fundamentals of QSARrdquo Accountsof Chemical Research vol 35 no 5 pp 289ndash295 2002

[26] D A Rincon M N D S Cordeiro and R A Mosquera ldquoOnthe electronic structure of cocaine and its metabolitesrdquo Journalof Physical Chemistry A vol 113 no 50 pp 13937ndash13942 2009

[27] M Mandado M J Gonzalez-Moa and R A MosqueraldquoChemical graph theory and n-center electron delocalization

indices a study on polycyclic aromatic hydrocarbonsrdquo Journalof Computational Chemistry vol 28 no 10 pp 1625ndash1633 2007

[28] THill andP Lewicki STATISTICSMethods andApplications AComprehensive Reference for Science Industry and DataMiningStatSoft Tulsa Okla USA 2006

[29] E Frank M Hall L Trigg G Holmes and I H Witten ldquoDatamining in bioinformatics using Wekardquo Bioinformatics vol 20no 15 pp 2479ndash2481 2004

[30] G Patlewicz N Ball E D Booth E Hulzebos E Zvinavasheand C Hennes ldquoUse of category approaches read-across and(Q)SAR general considerationsrdquo Regulatory Toxicology andPharmacology vol 67 no 1 pp 1ndash12 2013

[31] D W Roberts and G Y Patlewicz ldquoUpdating the skin sensiti-zation in vitro data assessment paradigm in 2009mdasha chemistryand QSAR perspectiverdquo Journal of Applied Toxicology vol 30no 3 pp 286ndash288 2010

[32] D W Roberts and G Y Patlewicz ldquoNonanimal alternatives forskin sensitization letter to the editorrdquoToxicological Sciences vol106 no 2 pp 572ndash574 2008

[33] E Estrada G Patlewicz and Y Gutierrez ldquoFrom knowledgegeneration to knowledge archive a general strategy usingTOPS-MODE with DEREK to formulate new alerts for skinsensitizationrdquo Journal of Chemical Information and ComputerSciences vol 44 no 2 pp 688ndash698 2004

[34] G F Gerberick C A Ryan P S Kern et al ldquoA chemical datasetfor evaluation of alternative approaches to skin-sensitizationtestingrdquo Contact Dermatitis vol 50 no 5 pp 274ndash288 2004

[35] G Y Patlewicz D A Basketter C K Smith Pease et al ldquoFurtherevaluation of quantitative structurendashactivity relationship mod-els for the prediction of the skin sensitization potency ofselected fragrance allergensrdquo Contact Dermatitis vol 50 no 2pp 91ndash97 2004

[36] E Estrada G Patlewicz M Chamberlain D Basketter andS Larbey ldquoComputer-aided knowledge generation for under-standing skin sensitization mechanisms the TOPS-MODEapproachrdquo Chemical Research in Toxicology vol 16 no 10 pp1226ndash1235 2003

[37] G Patlewicz R Rodford and J D Walker ldquoQuantitative struc-ture-activity relationships for predicting skin and eye irritationrdquoEnvironmental Toxicology and Chemistry vol 22 no 8 pp1862ndash1869 2003

[38] G Y Patlewicz R A Rodford G Ellis and M D Barratt ldquoAQSAR model for the eye irritation of cationic surfactantsrdquo Tox-icology In Vitro vol 14 no 1 pp 79ndash84 2000

[39] E Tenorio-Borroto C G Penuelas Rivas J C VasquezChagoyan N Castanedo F J Prado-Prado and X Garcia-Mera ldquoANN multiplexing model of drugs effect on macro-phages theoretical and flow cytometry study on the cyto-toxicity of the anti-microbial drug G1 in spleenrdquo Bioorganic ampMedicinal Chemistry vol 20 no 20 pp 6181ndash6194 2012

[40] D R Flower H McSparron M J Blythe et al ldquoComputationalvaccinology quantitative approachesrdquo Novartis FoundationSymposium vol 254 pp 102ndash120 2003

[41] I A Doytchinova P Guan and D R Flower ldquoQuantitativestructure-activity relationships and the prediction of MHCsupermotifsrdquoMethods vol 34 no 4 pp 444ndash453 2004

[42] Y Xiao and M R Segal ldquoPrediction of genomewide conservedepitope profiles of HIV-1 classifier choice and peptide rep-resentationrdquo Statistical Applications in Genetics and MolecularBiology vol 4 no 1 article 25 2005

Journal of Immunology Research 15

[43] T Fagerberg V Zoete S Viatte P Baumgaertner P M Alves PRomero et al ldquoPrediction of cross-recognition of peptide-HLAA2 by melan-a-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationshipsrdquo PLoSONE vol 8 no 7 Article ID e65590 2013

[44] D Barh A N Misra A Kumar and A Vasco ldquoA novel strategyof epitope design inNeisseria gonorrhoeaerdquo Bioinformation vol5 no 2 pp 77ndash82 2010

[45] J Bi R Song H Yang et al ldquoStepwise identification of HLA-Alowast0201-restricted CD8+ T-cell epitope peptides from herpessimplex virus type 1 genome boosted by a steprank schemerdquoBiopolymers vol 96 no 3 pp 328ndash339 2011

[46] R D Bremel and E J Homan ldquoAn integrated approach to epit-ope analysis II a system for proteomic-scale prediction ofimmunological characteristicsrdquo Immunome Research vol 6 no1 article 8 2010

[47] C M Diez-Rivero B Chenlo P Zuluaga and P A RecheldquoQuantitative modeling of peptide binding to TAP using sup-port vector machinerdquo Proteins vol 78 no 1 pp 63ndash72 2010

[48] E Martınez-Naves E M Lafuente and P A Reche ldquoRecogni-tion of the ligand-type specificity of classical and non-classicalMHC I proteinsrdquo FEBS Letters vol 585 no 21 pp 3478ndash34842011

[49] M Bhasin E L Reinherz and P A Reche ldquoRecognition andclassification of histones using support vectormachinerdquo Journalof Computational Biology vol 13 no 1 pp 102ndash112 2006

[50] P A Reche and E L Reinherz ldquoPEPVAC a web server formulti-epitope vaccine development based on the prediction ofsupertypic MHC ligandsrdquo Nucleic Acids Research vol 33 no 2pp W138ndashW142 2005

[51] E M Lafuente and P A Reche ldquoPrediction of MHC-peptidebinding a systematic and comprehensive overviewrdquo CurrentPharmaceutical Design vol 15 no 28 pp 3209ndash3220 2009

[52] A Gaulton L J Bellis A P Bento et al et al ldquoA large-scalebioactivity database for drug discoveryrdquoNucleic Acids Researchpp D1100ndashD1107 2012

[53] W H Cropper Great Physicists The Life and Times of LeadingPhysicists from Galileo to Hawking Oxford University PressNew York NY USA 2004

[54] E L Bouzarth A Brooks R Camassa et al ldquoEpicyclic orbits ina viscous fluid about a precessing rod theory and experimentsat the micro-and macro-scalesrdquo Physical Review E vol 76 no1 part 2 Article ID 016313 2007

[55] M Laberge ldquoIntrinsic protein electric fields basic non-covalentinteractions and relationship to protein-induced Stark effectsrdquoBiochimica et BiophysicaActa vol 1386 no 2 pp 305ndash330 1998

[56] FM Fernandez and J AMorales ldquoPerturbation theorywithoutwave functions for the Zeeman effect in hydrogenrdquo PhysicalReview A vol 46 no 1 pp 318ndash326 1992

[57] B D Marshall and W G Chapman ldquoThermodynamic pertur-bation theory for associating fluids with small bond angleseffects of steric hindrance ring formation and double bondingrdquoPhysical Reiew E vol 87 no 5 Article ID 052307 12 pages 2013

[58] H Gonzalez-Diaz S Arrasate A Gomez-San Juan et al ldquoNewtheory for multiple input-output perturbations in complexmolecular systems 1 Linear QSPR electronegativity models inphysical organic and medicinal chemistryrdquo Current Topics inMedicinal Chemistry 2013

[59] H Gonzalez-Diaz S Arrasate N Sotomayor et al ldquoMIANNmodels in medicinal physical and organic chemistryrdquo CurrentTopics in Medicinal Chemistry vol 13 no 5 pp 619ndash641 2013

[60] G E P Box and GM Jenkins Time Series Analysis Forecastingand Control Holden-Day San Francisco Calif USA 1970

[61] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoUnified multi-target approach for the rational insilico design of anti-bladder cancer agentsrdquo Anti-Cancer Agentsin Medicial Chemistry vol 13 no 5 pp 791ndash800 2013

[62] A Speck-Planche V V Kleandrova and M N Cordeiro ldquoNewinsights toward the discovery of antibacterial agents multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugsrdquo TheEuropean Journal of Pharmaceutical Sciences vol 48 no 4-5pp 812ndash818 2013

[63] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoMulti-target inhibitors for proteins associated withAlzheimer in silico discovery using fragment-based descrip-torsrdquo Current Alzheimer Research vol 10 no 2 pp 117ndash1242013

[64] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoIn silico discovery and virtual screening of multi-target inhibitors for proteins in Mycobacterium tuberculosisrdquoCombinatorial Chemistry amp High Throughput Screening vol 15no 8 pp 666ndash673 2012

[65] A Speck-Planche V V Kleandrova F Luan and M N DS Cordeiro ldquoChemoinformatics in anti-cancer chemotherapymulti-target QSAR model for the in silico discovery of anti-breast cancer agentsrdquo The European Journal of PharmaceuticalSciences vol 47 no 1 pp 273ndash279 2012

Submit your manuscripts athttpwwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MEDIATORSINFLAMMATION

of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Behavioural Neurology

EndocrinologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Disease Markers

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

OncologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Oxidative Medicine and Cellular Longevity

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational and Mathematical Methods in Medicine

OphthalmologyJournal of

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Diabetes ResearchJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Research and TreatmentAIDS

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Gastroenterology Research and Practice

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Page 11: Research Article Model for Vaccine Design by …downloads.hindawi.com/journals/jir/2014/768515.pdfResearch Article Model for Vaccine Design by Prediction of B-Epitopes of IEDB Given

Journal of Immunology Research 11Ta

ble3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

142228

LYCY

EQLN

DSSE

Hom

osapiens

Hum

anpapillomaviru

sNI

ELISA

142250

NWGDEP

SKRR

DRS

NSR

GRK

NFelis

catus

Felin

einfectio

usAID

ELISA

007

006

80071

0073

007

21549

GNYD

FWYQ

SHom

osapiens

Staphylococcus

aureus

OOID

ELISA

21553

GNYN

YKY

RYLR

HGK

LRPF

ERMus

musculus

SARS

coronavirus

AID

ELISA

003

0032

0021

0031

003

21549

GNYD

FWYQ

SHom

osapiens

Staphylococcus

aureus

OOID

ELISA

21553

GNYN

YKY

RYLR

HGK

LRPF

EROryctolagus

cuniculus

SARS

coronavirus

AID

ELISA

003

0034

0021

0031

003

34908

LAPL

GE

Hom

osapiens

HepatitisE

virus

EWEIR

ELISA

34914

LAPS

TLRS

LRKR

RLSSP

Hom

osapiens

Hum

anherpesvirus

OOID

ELISA

006

006

0059

0063

006

130454

CLFP

NNSY

CMus

musculus

MD

AID

NIAA

130456

CRPQ

VN

NPK

EWS

CAAC

Hom

osapiens

MD

OOD

ACAb

B001

0008

001

0011

0007

1964

4GFV

PSM

Hom

osapiens

Hepatitisd

elta

virus

OOID

ELISA

1964

7GFV

SASI

FGFQ

AEV

GPN

NTR

Oryctolagus

cuniculus

VacciniavirusW

RAID

ELISAminus001minus000

6minus0013minus000

9minus001

20678

GKRP

EMus

musculus

Streptococcus

pyogenes

AID

WI

20680

GKS

KRD

AKN

NAAK

LAVDKL

LMus

musculus

VacciniavirusW

RAID

WI

001

001

0001

001

001

123278

GYL

KDLP

TTOvisa

ries

Fasciolahepatica

AID

ELISA

123282

HAC

QKK

LLKF

EAL

QQEE

GEE

Rattu

snorvegicu

sGallusgallus

AID

PFFminus002minus0016minus0016minus002minus0025

141067

PLSLEP

DP

Mus

musculus

Hom

osapiens

AID

FACS

141073

REGVRW

RVMAIQ

Mus

musculus

Hom

osapiens

AID

ELISA

006

006

006

006

0056

139248

SFAG

TVIE

Mus

musculus

Classicalsw

ine

AID

WI

139305

TAAQ

ITQ

RKWEA

ARE

AEQ

RROryctolagus

cuniculus

Hom

osapiens

AID

IP003

0033

0026

003

003

104515

HDCR

PKKI

Mus

musculus

LaCrossevirus

AID

IFAIH

104520

IAKE

QE

NKE

TIGT

LKKI

LDE

Rattu

snorvegicu

sStreptococcus

pyogenes

AID

ELISAminus006minus006minus0073minus006minus0061

113517

HLY

ADGLT

DMus

musculus

Hum

anpapillomaviru

sAID

IFAIH

113518

HNKI

QA

IELE

DLL

RYS

KLYR

Mus

musculus

Hom

osapiens

AID

ELISA

002

002

0011

002

0019

156970

VER

HQ

Hom

osapiens

Hom

osapiens

OAID

ELISA

156975

WSSTV

LRVS

PTRT

VP

Mus

musculus

MD

AID

ELISA

001

0012

0003

0017

001

78252

PVQNLT

Mus

musculus

Porphyromonas

gingivalis

AID

WI

78253

QGGCG

RGWAFS

ATG

AIEA

Mus

musculus

Glycinem

axAID

ELISA

002

002

0017

002

0018

6068

CCPD

KNKS

Mus

musculus

Hum

anherpesvirus

AID

WI

6074

CCRH

KQKD

VGDVK

QTL

PPS

Ovisa

ries

MD

AID

ELISA

001

000

6000

4001

0008

12 Journal of Immunology Research

Table3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

53109

RAGVC

YHom

osapiens

Triticum

aestivum

OOA

ELISA

53116

RAILTA

FSPA

QDI

WGTS

Oryctolagus

cuniculus

SARS

coronavirus

AID

ELISAminus002minus0016minus0037minus0023minus002

147041

IPEQ

Hom

osapiens

Triticum

aestivum

OOA

WI

147064

KHQGA

QYV

WN

RTA

Hom

osapiens

Bostaurus

OOID

WI

006

006

0047

0057

006

7066

4VQ

VVYD

YQOryctolagus

cuniculus

Treponem

apallidu

mAID

ELISA

70667

VQWMN

RLIAFA

FAG

NHVS

PHom

osapiens

HepatitisC

virus

OOID

ELISA

005

004

6004

10049

005

134343

DLY

IKMus

musculus

Hum

anpapillomaviru

sAID

PhDIP

134344

DMAQ

VTV

GPG

LLGVS

TLMus

musculus

Hom

osapiens

AID

PhDIP

00minus000

90

0

Journal of Immunology Research 13

The first input term is the value 120582(120576119894119895)ref is the scoring

function 120582 of the efficiency of the initial process 120576119894119895(known

solution) The function 120582(120576119894119895)ref = 1 if the 119894th peptide could

experimentally be demonstrated to be a B-epitope in theassay of reference (reference) carried out in the conditions119888119895 120582(120576119894119895)ref = 0 otherwise The variational-perturbation

terms ΔΔ120594119888119895are at the same time terms typical of pertur-

bation theory and moving average (MA) functions used inBox-Jenkin models in time series [60] These new types ofterms account both for the deviation of the electronegativityof all amino acids in the sequence of the new peptidewith respect to the peptide of reference and with respectto all boundary conditions In Table 1 we give the overallclassification results obtainedwith thismodel Speck-Plancheet al [61ndash63] introduced different multitargetmultiplexingQSARmodels that incorporate this type of information basedon MAs The results obtained with the present model areexcellent comparedwith other similarmodels in the literatureuseful for other problems including moving average models[64 65] or perturbation models [58] Notably this is also thefirst model combining both perturbation theory and MAs ina QSPR context

The other input terms are the following The first Δ120594seq =120594(119898119902)ref minus120594(119898119894)new is the perturbation term for the variation

or in themean value of electronegativity for all amino acids inthe sequence of the peptide of reference The remnant inputvariables of the model ΔΔ120594

119888119895= Δ120594

119888119895-ref minus Δ120594119888119895-new =

[120594(119898119902)ref minus

lowast120594 (119888119902119903)ref] minus [120594(119898119894)new minus

lowast120594 (119888119894119895)new] quantify

values of the conditions of the new assay 119888119895-new that representperturbations with respect to the initial conditions 119888

119894119895-ref of

the assay of reference The quantities lowast120594(119888119894119895) and lowast120594(119888

119902119903) are

the average values of the mean electronegativity values 120594(119898119894)

and 120594(119898119902) for all new and reference peptides in IEDB that

are epitopes under the 119895th or 119903th boundary condition Thevalues of these terms have been tabulated for gt500 sourceorganisms gt50 host organisms gt10 biological process andgt30 experimental techniques We must substitute the valuesof 120594(119898

119894) and 120594(119898

119902) of the new and reference peptides and the

tabulated values of lowast120594(119888119894119895) and lowast120594(119888

119902119903) for all combinations

of boundary conditions to predict the perturbations of theaction as epitope of peptides In doing so we can found theoptimal sequence and boundary conditions towards the useof the peptide in the development of a vaccine In Table 2 wegive some of these values of lowast120594(119888

119894119895) and lowast120594(119888

119902119903)

In Table 3 we depict the sequences and input-outputboundary conditions for top perturbations present in IEDBAll these perturbations have observed value of 120582(120576

119894119895)new = 1

and predicted value also equal to 1 with a high probabilitySee Supplementary Material available online at httpdxdoiorg1011552014768515 file contains a full list of gt200000cases of perturbations

4 Conclusions

It is possible to develop generalmodels for vaccine design ableto predict the results of multiple input-output perturbations

in peptide sequence and experimental assay boundary condi-tions using ideas of QSPR analysis perturbation theory andBox and Jenkins MA operators The electronegativity valuescalculated withMARCH-INSIDE seem to be goodmoleculardescriptors for this type of QSPR-perturbation models

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

The present study was partially supported by GrantsAGL2010-22290-C02 and AGL2011-30563-C03 from Minis-terio de Ciencia e Innovacion Spain and Grant CN 2012155from Xunta de Galicia Spain

References

[1] B Peters J Sidney P Bourne et al ldquoThe design and implemen-tation of the immune epitope database and analysis resourcerdquoImmunogenetics vol 57 no 5 pp 326ndash336 2005

[2] B Peters J Sidney P Bourne et al ldquoThe immune epitopedatabase and analysis resource from vision to blueprintrdquo PLoSBiology vol 3 no 3 p e91 2005

[3] PWang A AMorgan Q Zhang A Sette and B Peters ldquoAuto-mating document classification for the Immune Epitope Data-baserdquo BMC Bioinformatics vol 8 article 269 2007

[4] A Sette ldquoThe immune epitope database and analysis resourcefrom vision to blueprintrdquo Genome Informatics vol 15 no 2 p299 2004

[5] N Salimi W Fleri B Peters and A Sette ldquoThe immune epi-tope database a historical retrospective of the first decaderdquoImmunology vol 137 no 2 pp 117ndash123 2012

[6] Y Kim A Sette and B Peters ldquoApplications for T-cell epitopequeries and tools in the Immune EpitopeDatabase andAnalysisResourcerdquo Journal of Immunological Methods vol 374 no 1-2pp 62ndash69 2011

[7] Y Kim J Ponomarenko Z Zhu D Tamang et al ldquoImmuneepitope database analysis resourcerdquo Nucleic Acids Research ppW525ndashW530 2012

[8] A M Helguera R D Combes M P Gonzalez and M N DS Cordeiro ldquoApplications of 2D descriptors in drug design aDRAGON talerdquo Current Topics in Medicinal Chemistry vol 8no 18 pp 1628ndash1655 2008

[9] GM Casanola-Martın Y Marrero-Ponce M T H Khan et alldquoDragon method for finding novel tyrosinase inhibitors Biosil-ico identification and experimental in vitro assaysrdquo The Euro-pean Journal of Medicinal Chemistry vol 42 no 11-12 pp 1370ndash1381 2007

[10] I V Tetko J Gasteiger R Todeschini et al ldquoVirtual computa-tional chemistry laboratorymdashdesign and descriptionrdquo Journalof Computer-AidedMolecular Design vol 19 no 6 pp 453ndash4632005

[11] A R Katritzky A Oliferenko A Lomaka and M KarelsonldquoSix-membered cyclic ureas as HIV-1 protease inhibitors aQSAR study based on CODESSA PRO approachrdquo Bioorganicand Medicinal Chemistry Letters vol 12 no 23 pp 3453ndash34572002

14 Journal of Immunology Research

[12] A R Katritzky S Perumal R Petrukhin and E Klein-peter ldquoCODESSA-based theoretical QSPRmodel for hydantoinHPLC-RT lipophilicitiesrdquo Journal of Chemical Information andComputer Sciences vol 41 no 3 pp 569ndash574 2001

[13] S Vilar G Cozza and S Moro ldquoMedicinal chemistry and themolecular operating environment (MOE) application of QSARand molecular docking to drug discoveryrdquo Current Topics inMedicinal Chemistry vol 8 no 18 pp 1555ndash1572 2008

[14] G Marzaro A Chilin A Guiotto et al ldquoUsing the TOPS-MODE approach to fit multi-target QSAR models for tyrosinekinases inhibitorsrdquo The European Journal of Medicinal Chem-istry vol 46 no 6 pp 2185ndash2192 2011

[15] S Vilar E Estrada E Uriarte L Santana and Y Gutierrez ldquoInsilico studies toward the discovery of new anti-HIV nucleosidecompounds through the use of tops-mode and 2D3D con-nectivity indices 2 Purine derivativesrdquo Journal of ChemicalInformation and Modeling vol 45 no 2 pp 502ndash514 2005

[16] E Estrada J A Quincoces and G Patlewicz ldquoCreating molec-ular diversity from antioxidants in Brazilian propolis combina-tion of TOPS-MODE QSAR and virtual structure generationrdquoMolecular Diversity vol 8 no 1 pp 21ndash33 2004

[17] E Estrada andHGonzalez ldquoWhat are the limits of applicabilityfor graph theoretic descriptors in QSPRQSAR modelingdipole moments of aromatic compounds with TOPS-MODEdescriptorsrdquo Journal of Chemical Information and ComputerSciences vol 43 no 1 pp 75ndash84 2003

[18] Y Marrero-Ponce J A Castillo-Garit E Olazabal et al ldquoTom-ocomd-Cardd a novel approach for computer-aided ldquorationalrdquodrug design I Theoretical and experimental assessment of apromising method for computational screening and in silicodesign of new anthelmintic compoundsrdquo Journal of Computer-Aided Molecular Design vol 18 no 10 pp 615ndash634 2004

[19] Y Marrero Ponce R Medina Marrero E A Castro et al ldquoPro-tein quadratic indices of the ldquomacromolecular pseudographrsquos 120572-carbon atom adjacency matrixrdquo 1 prediction of arc repressoralanine-mutantrsquos stabilityrdquoMolecules vol 9 no 12 pp 1124ndash11472004

[20] H Gonzalez-Dıaz F Prado-Prado and F M Ubeira ldquoPredict-ing antimicrobial drugs and targets with the MARCH-INSIDEapproachrdquo Current Topics in Medicinal Chemistry vol 8 no 18pp 1676ndash1690 2008

[21] R Garcıa-Domenech J Galvez J V de Julian-Ortiz and LPogliani ldquoSomenew trends in chemical graph theoryrdquoChemicalReviews vol 108 no 3 pp 1127ndash1169 2008

[22] E Estrada and E Uriarte ldquoRecent advances on the role of topo-logical indices in drug discovery researchrdquo Current MedicinalChemistry vol 8 no 13 pp 1573ndash1588 2001

[23] R Todeschini andVConsonniHandbook ofMolecularDescrip-tors Wiley-VCH Weinheim Germany 2008

[24] E Estrada E J Delgado J B Alderete and G A Jana ldquoQuan-tum-connectivity descriptors inmodeling solubility of environ-mentally important organic compoundsrdquo Journal of Computa-tional Chemistry vol 25 no 14 pp 1787ndash1796 2004

[25] E Besalu X Girones L Amat andR Carbo-Dorca ldquoMolecularquantum similarity and the fundamentals of QSARrdquo Accountsof Chemical Research vol 35 no 5 pp 289ndash295 2002

[26] D A Rincon M N D S Cordeiro and R A Mosquera ldquoOnthe electronic structure of cocaine and its metabolitesrdquo Journalof Physical Chemistry A vol 113 no 50 pp 13937ndash13942 2009

[27] M Mandado M J Gonzalez-Moa and R A MosqueraldquoChemical graph theory and n-center electron delocalization

indices a study on polycyclic aromatic hydrocarbonsrdquo Journalof Computational Chemistry vol 28 no 10 pp 1625ndash1633 2007

[28] THill andP Lewicki STATISTICSMethods andApplications AComprehensive Reference for Science Industry and DataMiningStatSoft Tulsa Okla USA 2006

[29] E Frank M Hall L Trigg G Holmes and I H Witten ldquoDatamining in bioinformatics using Wekardquo Bioinformatics vol 20no 15 pp 2479ndash2481 2004

[30] G Patlewicz N Ball E D Booth E Hulzebos E Zvinavasheand C Hennes ldquoUse of category approaches read-across and(Q)SAR general considerationsrdquo Regulatory Toxicology andPharmacology vol 67 no 1 pp 1ndash12 2013

[31] D W Roberts and G Y Patlewicz ldquoUpdating the skin sensiti-zation in vitro data assessment paradigm in 2009mdasha chemistryand QSAR perspectiverdquo Journal of Applied Toxicology vol 30no 3 pp 286ndash288 2010

[32] D W Roberts and G Y Patlewicz ldquoNonanimal alternatives forskin sensitization letter to the editorrdquoToxicological Sciences vol106 no 2 pp 572ndash574 2008

[33] E Estrada G Patlewicz and Y Gutierrez ldquoFrom knowledgegeneration to knowledge archive a general strategy usingTOPS-MODE with DEREK to formulate new alerts for skinsensitizationrdquo Journal of Chemical Information and ComputerSciences vol 44 no 2 pp 688ndash698 2004

[34] G F Gerberick C A Ryan P S Kern et al ldquoA chemical datasetfor evaluation of alternative approaches to skin-sensitizationtestingrdquo Contact Dermatitis vol 50 no 5 pp 274ndash288 2004

[35] G Y Patlewicz D A Basketter C K Smith Pease et al ldquoFurtherevaluation of quantitative structurendashactivity relationship mod-els for the prediction of the skin sensitization potency ofselected fragrance allergensrdquo Contact Dermatitis vol 50 no 2pp 91ndash97 2004

[36] E Estrada G Patlewicz M Chamberlain D Basketter andS Larbey ldquoComputer-aided knowledge generation for under-standing skin sensitization mechanisms the TOPS-MODEapproachrdquo Chemical Research in Toxicology vol 16 no 10 pp1226ndash1235 2003

[37] G Patlewicz R Rodford and J D Walker ldquoQuantitative struc-ture-activity relationships for predicting skin and eye irritationrdquoEnvironmental Toxicology and Chemistry vol 22 no 8 pp1862ndash1869 2003

[38] G Y Patlewicz R A Rodford G Ellis and M D Barratt ldquoAQSAR model for the eye irritation of cationic surfactantsrdquo Tox-icology In Vitro vol 14 no 1 pp 79ndash84 2000

[39] E Tenorio-Borroto C G Penuelas Rivas J C VasquezChagoyan N Castanedo F J Prado-Prado and X Garcia-Mera ldquoANN multiplexing model of drugs effect on macro-phages theoretical and flow cytometry study on the cyto-toxicity of the anti-microbial drug G1 in spleenrdquo Bioorganic ampMedicinal Chemistry vol 20 no 20 pp 6181ndash6194 2012

[40] D R Flower H McSparron M J Blythe et al ldquoComputationalvaccinology quantitative approachesrdquo Novartis FoundationSymposium vol 254 pp 102ndash120 2003

[41] I A Doytchinova P Guan and D R Flower ldquoQuantitativestructure-activity relationships and the prediction of MHCsupermotifsrdquoMethods vol 34 no 4 pp 444ndash453 2004

[42] Y Xiao and M R Segal ldquoPrediction of genomewide conservedepitope profiles of HIV-1 classifier choice and peptide rep-resentationrdquo Statistical Applications in Genetics and MolecularBiology vol 4 no 1 article 25 2005

Journal of Immunology Research 15

[43] T Fagerberg V Zoete S Viatte P Baumgaertner P M Alves PRomero et al ldquoPrediction of cross-recognition of peptide-HLAA2 by melan-a-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationshipsrdquo PLoSONE vol 8 no 7 Article ID e65590 2013

[44] D Barh A N Misra A Kumar and A Vasco ldquoA novel strategyof epitope design inNeisseria gonorrhoeaerdquo Bioinformation vol5 no 2 pp 77ndash82 2010

[45] J Bi R Song H Yang et al ldquoStepwise identification of HLA-Alowast0201-restricted CD8+ T-cell epitope peptides from herpessimplex virus type 1 genome boosted by a steprank schemerdquoBiopolymers vol 96 no 3 pp 328ndash339 2011

[46] R D Bremel and E J Homan ldquoAn integrated approach to epit-ope analysis II a system for proteomic-scale prediction ofimmunological characteristicsrdquo Immunome Research vol 6 no1 article 8 2010

[47] C M Diez-Rivero B Chenlo P Zuluaga and P A RecheldquoQuantitative modeling of peptide binding to TAP using sup-port vector machinerdquo Proteins vol 78 no 1 pp 63ndash72 2010

[48] E Martınez-Naves E M Lafuente and P A Reche ldquoRecogni-tion of the ligand-type specificity of classical and non-classicalMHC I proteinsrdquo FEBS Letters vol 585 no 21 pp 3478ndash34842011

[49] M Bhasin E L Reinherz and P A Reche ldquoRecognition andclassification of histones using support vectormachinerdquo Journalof Computational Biology vol 13 no 1 pp 102ndash112 2006

[50] P A Reche and E L Reinherz ldquoPEPVAC a web server formulti-epitope vaccine development based on the prediction ofsupertypic MHC ligandsrdquo Nucleic Acids Research vol 33 no 2pp W138ndashW142 2005

[51] E M Lafuente and P A Reche ldquoPrediction of MHC-peptidebinding a systematic and comprehensive overviewrdquo CurrentPharmaceutical Design vol 15 no 28 pp 3209ndash3220 2009

[52] A Gaulton L J Bellis A P Bento et al et al ldquoA large-scalebioactivity database for drug discoveryrdquoNucleic Acids Researchpp D1100ndashD1107 2012

[53] W H Cropper Great Physicists The Life and Times of LeadingPhysicists from Galileo to Hawking Oxford University PressNew York NY USA 2004

[54] E L Bouzarth A Brooks R Camassa et al ldquoEpicyclic orbits ina viscous fluid about a precessing rod theory and experimentsat the micro-and macro-scalesrdquo Physical Review E vol 76 no1 part 2 Article ID 016313 2007

[55] M Laberge ldquoIntrinsic protein electric fields basic non-covalentinteractions and relationship to protein-induced Stark effectsrdquoBiochimica et BiophysicaActa vol 1386 no 2 pp 305ndash330 1998

[56] FM Fernandez and J AMorales ldquoPerturbation theorywithoutwave functions for the Zeeman effect in hydrogenrdquo PhysicalReview A vol 46 no 1 pp 318ndash326 1992

[57] B D Marshall and W G Chapman ldquoThermodynamic pertur-bation theory for associating fluids with small bond angleseffects of steric hindrance ring formation and double bondingrdquoPhysical Reiew E vol 87 no 5 Article ID 052307 12 pages 2013

[58] H Gonzalez-Diaz S Arrasate A Gomez-San Juan et al ldquoNewtheory for multiple input-output perturbations in complexmolecular systems 1 Linear QSPR electronegativity models inphysical organic and medicinal chemistryrdquo Current Topics inMedicinal Chemistry 2013

[59] H Gonzalez-Diaz S Arrasate N Sotomayor et al ldquoMIANNmodels in medicinal physical and organic chemistryrdquo CurrentTopics in Medicinal Chemistry vol 13 no 5 pp 619ndash641 2013

[60] G E P Box and GM Jenkins Time Series Analysis Forecastingand Control Holden-Day San Francisco Calif USA 1970

[61] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoUnified multi-target approach for the rational insilico design of anti-bladder cancer agentsrdquo Anti-Cancer Agentsin Medicial Chemistry vol 13 no 5 pp 791ndash800 2013

[62] A Speck-Planche V V Kleandrova and M N Cordeiro ldquoNewinsights toward the discovery of antibacterial agents multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugsrdquo TheEuropean Journal of Pharmaceutical Sciences vol 48 no 4-5pp 812ndash818 2013

[63] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoMulti-target inhibitors for proteins associated withAlzheimer in silico discovery using fragment-based descrip-torsrdquo Current Alzheimer Research vol 10 no 2 pp 117ndash1242013

[64] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoIn silico discovery and virtual screening of multi-target inhibitors for proteins in Mycobacterium tuberculosisrdquoCombinatorial Chemistry amp High Throughput Screening vol 15no 8 pp 666ndash673 2012

[65] A Speck-Planche V V Kleandrova F Luan and M N DS Cordeiro ldquoChemoinformatics in anti-cancer chemotherapymulti-target QSAR model for the in silico discovery of anti-breast cancer agentsrdquo The European Journal of PharmaceuticalSciences vol 47 no 1 pp 273ndash279 2012

Submit your manuscripts athttpwwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MEDIATORSINFLAMMATION

of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Behavioural Neurology

EndocrinologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Disease Markers

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

OncologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Oxidative Medicine and Cellular Longevity

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational and Mathematical Methods in Medicine

OphthalmologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Diabetes ResearchJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Research and TreatmentAIDS

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Gastroenterology Research and Practice

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Page 12: Research Article Model for Vaccine Design by …downloads.hindawi.com/journals/jir/2014/768515.pdfResearch Article Model for Vaccine Design by Prediction of B-Epitopes of IEDB Given

12 Journal of Immunology Research

Table3Con

tinued

New

experim

ent

Experim

ento

freference

Inpu

tperturbationterm

sID

ESequ

ence

hoso

iptq

IDE

Sequ

ence

hoso

iptq

Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq

53109

RAGVC

YHom

osapiens

Triticum

aestivum

OOA

ELISA

53116

RAILTA

FSPA

QDI

WGTS

Oryctolagus

cuniculus

SARS

coronavirus

AID

ELISAminus002minus0016minus0037minus0023minus002

147041

IPEQ

Hom

osapiens

Triticum

aestivum

OOA

WI

147064

KHQGA

QYV

WN

RTA

Hom

osapiens

Bostaurus

OOID

WI

006

006

0047

0057

006

7066

4VQ

VVYD

YQOryctolagus

cuniculus

Treponem

apallidu

mAID

ELISA

70667

VQWMN

RLIAFA

FAG

NHVS

PHom

osapiens

HepatitisC

virus

OOID

ELISA

005

004

6004

10049

005

134343

DLY

IKMus

musculus

Hum

anpapillomaviru

sAID

PhDIP

134344

DMAQ

VTV

GPG

LLGVS

TLMus

musculus

Hom

osapiens

AID

PhDIP

00minus000

90

0

Journal of Immunology Research 13

The first input term is the value 120582(120576119894119895)ref is the scoring

function 120582 of the efficiency of the initial process 120576119894119895(known

solution) The function 120582(120576119894119895)ref = 1 if the 119894th peptide could

experimentally be demonstrated to be a B-epitope in theassay of reference (reference) carried out in the conditions119888119895 120582(120576119894119895)ref = 0 otherwise The variational-perturbation

terms ΔΔ120594119888119895are at the same time terms typical of pertur-

bation theory and moving average (MA) functions used inBox-Jenkin models in time series [60] These new types ofterms account both for the deviation of the electronegativityof all amino acids in the sequence of the new peptidewith respect to the peptide of reference and with respectto all boundary conditions In Table 1 we give the overallclassification results obtainedwith thismodel Speck-Plancheet al [61ndash63] introduced different multitargetmultiplexingQSARmodels that incorporate this type of information basedon MAs The results obtained with the present model areexcellent comparedwith other similarmodels in the literatureuseful for other problems including moving average models[64 65] or perturbation models [58] Notably this is also thefirst model combining both perturbation theory and MAs ina QSPR context

The other input terms are the following The first Δ120594seq =120594(119898119902)ref minus120594(119898119894)new is the perturbation term for the variation

or in themean value of electronegativity for all amino acids inthe sequence of the peptide of reference The remnant inputvariables of the model ΔΔ120594

119888119895= Δ120594

119888119895-ref minus Δ120594119888119895-new =

[120594(119898119902)ref minus

lowast120594 (119888119902119903)ref] minus [120594(119898119894)new minus

lowast120594 (119888119894119895)new] quantify

values of the conditions of the new assay 119888119895-new that representperturbations with respect to the initial conditions 119888

119894119895-ref of

the assay of reference The quantities lowast120594(119888119894119895) and lowast120594(119888

119902119903) are

the average values of the mean electronegativity values 120594(119898119894)

and 120594(119898119902) for all new and reference peptides in IEDB that

are epitopes under the 119895th or 119903th boundary condition Thevalues of these terms have been tabulated for gt500 sourceorganisms gt50 host organisms gt10 biological process andgt30 experimental techniques We must substitute the valuesof 120594(119898

119894) and 120594(119898

119902) of the new and reference peptides and the

tabulated values of lowast120594(119888119894119895) and lowast120594(119888

119902119903) for all combinations

of boundary conditions to predict the perturbations of theaction as epitope of peptides In doing so we can found theoptimal sequence and boundary conditions towards the useof the peptide in the development of a vaccine In Table 2 wegive some of these values of lowast120594(119888

119894119895) and lowast120594(119888

119902119903)

In Table 3 we depict the sequences and input-outputboundary conditions for top perturbations present in IEDBAll these perturbations have observed value of 120582(120576

119894119895)new = 1

and predicted value also equal to 1 with a high probabilitySee Supplementary Material available online at httpdxdoiorg1011552014768515 file contains a full list of gt200000cases of perturbations

4 Conclusions

It is possible to develop generalmodels for vaccine design ableto predict the results of multiple input-output perturbations

in peptide sequence and experimental assay boundary condi-tions using ideas of QSPR analysis perturbation theory andBox and Jenkins MA operators The electronegativity valuescalculated withMARCH-INSIDE seem to be goodmoleculardescriptors for this type of QSPR-perturbation models

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

The present study was partially supported by GrantsAGL2010-22290-C02 and AGL2011-30563-C03 from Minis-terio de Ciencia e Innovacion Spain and Grant CN 2012155from Xunta de Galicia Spain

References

[1] B Peters J Sidney P Bourne et al ldquoThe design and implemen-tation of the immune epitope database and analysis resourcerdquoImmunogenetics vol 57 no 5 pp 326ndash336 2005

[2] B Peters J Sidney P Bourne et al ldquoThe immune epitopedatabase and analysis resource from vision to blueprintrdquo PLoSBiology vol 3 no 3 p e91 2005

[3] PWang A AMorgan Q Zhang A Sette and B Peters ldquoAuto-mating document classification for the Immune Epitope Data-baserdquo BMC Bioinformatics vol 8 article 269 2007

[4] A Sette ldquoThe immune epitope database and analysis resourcefrom vision to blueprintrdquo Genome Informatics vol 15 no 2 p299 2004

[5] N Salimi W Fleri B Peters and A Sette ldquoThe immune epi-tope database a historical retrospective of the first decaderdquoImmunology vol 137 no 2 pp 117ndash123 2012

[6] Y Kim A Sette and B Peters ldquoApplications for T-cell epitopequeries and tools in the Immune EpitopeDatabase andAnalysisResourcerdquo Journal of Immunological Methods vol 374 no 1-2pp 62ndash69 2011

[7] Y Kim J Ponomarenko Z Zhu D Tamang et al ldquoImmuneepitope database analysis resourcerdquo Nucleic Acids Research ppW525ndashW530 2012

[8] A M Helguera R D Combes M P Gonzalez and M N DS Cordeiro ldquoApplications of 2D descriptors in drug design aDRAGON talerdquo Current Topics in Medicinal Chemistry vol 8no 18 pp 1628ndash1655 2008

[9] GM Casanola-Martın Y Marrero-Ponce M T H Khan et alldquoDragon method for finding novel tyrosinase inhibitors Biosil-ico identification and experimental in vitro assaysrdquo The Euro-pean Journal of Medicinal Chemistry vol 42 no 11-12 pp 1370ndash1381 2007

[10] I V Tetko J Gasteiger R Todeschini et al ldquoVirtual computa-tional chemistry laboratorymdashdesign and descriptionrdquo Journalof Computer-AidedMolecular Design vol 19 no 6 pp 453ndash4632005

[11] A R Katritzky A Oliferenko A Lomaka and M KarelsonldquoSix-membered cyclic ureas as HIV-1 protease inhibitors aQSAR study based on CODESSA PRO approachrdquo Bioorganicand Medicinal Chemistry Letters vol 12 no 23 pp 3453ndash34572002

14 Journal of Immunology Research

[12] A R Katritzky S Perumal R Petrukhin and E Klein-peter ldquoCODESSA-based theoretical QSPRmodel for hydantoinHPLC-RT lipophilicitiesrdquo Journal of Chemical Information andComputer Sciences vol 41 no 3 pp 569ndash574 2001

[13] S Vilar G Cozza and S Moro ldquoMedicinal chemistry and themolecular operating environment (MOE) application of QSARand molecular docking to drug discoveryrdquo Current Topics inMedicinal Chemistry vol 8 no 18 pp 1555ndash1572 2008

[14] G Marzaro A Chilin A Guiotto et al ldquoUsing the TOPS-MODE approach to fit multi-target QSAR models for tyrosinekinases inhibitorsrdquo The European Journal of Medicinal Chem-istry vol 46 no 6 pp 2185ndash2192 2011

[15] S Vilar E Estrada E Uriarte L Santana and Y Gutierrez ldquoInsilico studies toward the discovery of new anti-HIV nucleosidecompounds through the use of tops-mode and 2D3D con-nectivity indices 2 Purine derivativesrdquo Journal of ChemicalInformation and Modeling vol 45 no 2 pp 502ndash514 2005

[16] E Estrada J A Quincoces and G Patlewicz ldquoCreating molec-ular diversity from antioxidants in Brazilian propolis combina-tion of TOPS-MODE QSAR and virtual structure generationrdquoMolecular Diversity vol 8 no 1 pp 21ndash33 2004

[17] E Estrada andHGonzalez ldquoWhat are the limits of applicabilityfor graph theoretic descriptors in QSPRQSAR modelingdipole moments of aromatic compounds with TOPS-MODEdescriptorsrdquo Journal of Chemical Information and ComputerSciences vol 43 no 1 pp 75ndash84 2003

[18] Y Marrero-Ponce J A Castillo-Garit E Olazabal et al ldquoTom-ocomd-Cardd a novel approach for computer-aided ldquorationalrdquodrug design I Theoretical and experimental assessment of apromising method for computational screening and in silicodesign of new anthelmintic compoundsrdquo Journal of Computer-Aided Molecular Design vol 18 no 10 pp 615ndash634 2004

[19] Y Marrero Ponce R Medina Marrero E A Castro et al ldquoPro-tein quadratic indices of the ldquomacromolecular pseudographrsquos 120572-carbon atom adjacency matrixrdquo 1 prediction of arc repressoralanine-mutantrsquos stabilityrdquoMolecules vol 9 no 12 pp 1124ndash11472004

[20] H Gonzalez-Dıaz F Prado-Prado and F M Ubeira ldquoPredict-ing antimicrobial drugs and targets with the MARCH-INSIDEapproachrdquo Current Topics in Medicinal Chemistry vol 8 no 18pp 1676ndash1690 2008

[21] R Garcıa-Domenech J Galvez J V de Julian-Ortiz and LPogliani ldquoSomenew trends in chemical graph theoryrdquoChemicalReviews vol 108 no 3 pp 1127ndash1169 2008

[22] E Estrada and E Uriarte ldquoRecent advances on the role of topo-logical indices in drug discovery researchrdquo Current MedicinalChemistry vol 8 no 13 pp 1573ndash1588 2001

[23] R Todeschini andVConsonniHandbook ofMolecularDescrip-tors Wiley-VCH Weinheim Germany 2008

[24] E Estrada E J Delgado J B Alderete and G A Jana ldquoQuan-tum-connectivity descriptors inmodeling solubility of environ-mentally important organic compoundsrdquo Journal of Computa-tional Chemistry vol 25 no 14 pp 1787ndash1796 2004

[25] E Besalu X Girones L Amat andR Carbo-Dorca ldquoMolecularquantum similarity and the fundamentals of QSARrdquo Accountsof Chemical Research vol 35 no 5 pp 289ndash295 2002

[26] D A Rincon M N D S Cordeiro and R A Mosquera ldquoOnthe electronic structure of cocaine and its metabolitesrdquo Journalof Physical Chemistry A vol 113 no 50 pp 13937ndash13942 2009

[27] M Mandado M J Gonzalez-Moa and R A MosqueraldquoChemical graph theory and n-center electron delocalization

indices a study on polycyclic aromatic hydrocarbonsrdquo Journalof Computational Chemistry vol 28 no 10 pp 1625ndash1633 2007

[28] THill andP Lewicki STATISTICSMethods andApplications AComprehensive Reference for Science Industry and DataMiningStatSoft Tulsa Okla USA 2006

[29] E Frank M Hall L Trigg G Holmes and I H Witten ldquoDatamining in bioinformatics using Wekardquo Bioinformatics vol 20no 15 pp 2479ndash2481 2004

[30] G Patlewicz N Ball E D Booth E Hulzebos E Zvinavasheand C Hennes ldquoUse of category approaches read-across and(Q)SAR general considerationsrdquo Regulatory Toxicology andPharmacology vol 67 no 1 pp 1ndash12 2013

[31] D W Roberts and G Y Patlewicz ldquoUpdating the skin sensiti-zation in vitro data assessment paradigm in 2009mdasha chemistryand QSAR perspectiverdquo Journal of Applied Toxicology vol 30no 3 pp 286ndash288 2010

[32] D W Roberts and G Y Patlewicz ldquoNonanimal alternatives forskin sensitization letter to the editorrdquoToxicological Sciences vol106 no 2 pp 572ndash574 2008

[33] E Estrada G Patlewicz and Y Gutierrez ldquoFrom knowledgegeneration to knowledge archive a general strategy usingTOPS-MODE with DEREK to formulate new alerts for skinsensitizationrdquo Journal of Chemical Information and ComputerSciences vol 44 no 2 pp 688ndash698 2004

[34] G F Gerberick C A Ryan P S Kern et al ldquoA chemical datasetfor evaluation of alternative approaches to skin-sensitizationtestingrdquo Contact Dermatitis vol 50 no 5 pp 274ndash288 2004

[35] G Y Patlewicz D A Basketter C K Smith Pease et al ldquoFurtherevaluation of quantitative structurendashactivity relationship mod-els for the prediction of the skin sensitization potency ofselected fragrance allergensrdquo Contact Dermatitis vol 50 no 2pp 91ndash97 2004

[36] E Estrada G Patlewicz M Chamberlain D Basketter andS Larbey ldquoComputer-aided knowledge generation for under-standing skin sensitization mechanisms the TOPS-MODEapproachrdquo Chemical Research in Toxicology vol 16 no 10 pp1226ndash1235 2003

[37] G Patlewicz R Rodford and J D Walker ldquoQuantitative struc-ture-activity relationships for predicting skin and eye irritationrdquoEnvironmental Toxicology and Chemistry vol 22 no 8 pp1862ndash1869 2003

[38] G Y Patlewicz R A Rodford G Ellis and M D Barratt ldquoAQSAR model for the eye irritation of cationic surfactantsrdquo Tox-icology In Vitro vol 14 no 1 pp 79ndash84 2000

[39] E Tenorio-Borroto C G Penuelas Rivas J C VasquezChagoyan N Castanedo F J Prado-Prado and X Garcia-Mera ldquoANN multiplexing model of drugs effect on macro-phages theoretical and flow cytometry study on the cyto-toxicity of the anti-microbial drug G1 in spleenrdquo Bioorganic ampMedicinal Chemistry vol 20 no 20 pp 6181ndash6194 2012

[40] D R Flower H McSparron M J Blythe et al ldquoComputationalvaccinology quantitative approachesrdquo Novartis FoundationSymposium vol 254 pp 102ndash120 2003

[41] I A Doytchinova P Guan and D R Flower ldquoQuantitativestructure-activity relationships and the prediction of MHCsupermotifsrdquoMethods vol 34 no 4 pp 444ndash453 2004

[42] Y Xiao and M R Segal ldquoPrediction of genomewide conservedepitope profiles of HIV-1 classifier choice and peptide rep-resentationrdquo Statistical Applications in Genetics and MolecularBiology vol 4 no 1 article 25 2005

Journal of Immunology Research 15

[43] T Fagerberg V Zoete S Viatte P Baumgaertner P M Alves PRomero et al ldquoPrediction of cross-recognition of peptide-HLAA2 by melan-a-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationshipsrdquo PLoSONE vol 8 no 7 Article ID e65590 2013

[44] D Barh A N Misra A Kumar and A Vasco ldquoA novel strategyof epitope design inNeisseria gonorrhoeaerdquo Bioinformation vol5 no 2 pp 77ndash82 2010

[45] J Bi R Song H Yang et al ldquoStepwise identification of HLA-Alowast0201-restricted CD8+ T-cell epitope peptides from herpessimplex virus type 1 genome boosted by a steprank schemerdquoBiopolymers vol 96 no 3 pp 328ndash339 2011

[46] R D Bremel and E J Homan ldquoAn integrated approach to epit-ope analysis II a system for proteomic-scale prediction ofimmunological characteristicsrdquo Immunome Research vol 6 no1 article 8 2010

[47] C M Diez-Rivero B Chenlo P Zuluaga and P A RecheldquoQuantitative modeling of peptide binding to TAP using sup-port vector machinerdquo Proteins vol 78 no 1 pp 63ndash72 2010

[48] E Martınez-Naves E M Lafuente and P A Reche ldquoRecogni-tion of the ligand-type specificity of classical and non-classicalMHC I proteinsrdquo FEBS Letters vol 585 no 21 pp 3478ndash34842011

[49] M Bhasin E L Reinherz and P A Reche ldquoRecognition andclassification of histones using support vectormachinerdquo Journalof Computational Biology vol 13 no 1 pp 102ndash112 2006

[50] P A Reche and E L Reinherz ldquoPEPVAC a web server formulti-epitope vaccine development based on the prediction ofsupertypic MHC ligandsrdquo Nucleic Acids Research vol 33 no 2pp W138ndashW142 2005

[51] E M Lafuente and P A Reche ldquoPrediction of MHC-peptidebinding a systematic and comprehensive overviewrdquo CurrentPharmaceutical Design vol 15 no 28 pp 3209ndash3220 2009

[52] A Gaulton L J Bellis A P Bento et al et al ldquoA large-scalebioactivity database for drug discoveryrdquoNucleic Acids Researchpp D1100ndashD1107 2012

[53] W H Cropper Great Physicists The Life and Times of LeadingPhysicists from Galileo to Hawking Oxford University PressNew York NY USA 2004

[54] E L Bouzarth A Brooks R Camassa et al ldquoEpicyclic orbits ina viscous fluid about a precessing rod theory and experimentsat the micro-and macro-scalesrdquo Physical Review E vol 76 no1 part 2 Article ID 016313 2007

[55] M Laberge ldquoIntrinsic protein electric fields basic non-covalentinteractions and relationship to protein-induced Stark effectsrdquoBiochimica et BiophysicaActa vol 1386 no 2 pp 305ndash330 1998

[56] FM Fernandez and J AMorales ldquoPerturbation theorywithoutwave functions for the Zeeman effect in hydrogenrdquo PhysicalReview A vol 46 no 1 pp 318ndash326 1992

[57] B D Marshall and W G Chapman ldquoThermodynamic pertur-bation theory for associating fluids with small bond angleseffects of steric hindrance ring formation and double bondingrdquoPhysical Reiew E vol 87 no 5 Article ID 052307 12 pages 2013

[58] H Gonzalez-Diaz S Arrasate A Gomez-San Juan et al ldquoNewtheory for multiple input-output perturbations in complexmolecular systems 1 Linear QSPR electronegativity models inphysical organic and medicinal chemistryrdquo Current Topics inMedicinal Chemistry 2013

[59] H Gonzalez-Diaz S Arrasate N Sotomayor et al ldquoMIANNmodels in medicinal physical and organic chemistryrdquo CurrentTopics in Medicinal Chemistry vol 13 no 5 pp 619ndash641 2013

[60] G E P Box and GM Jenkins Time Series Analysis Forecastingand Control Holden-Day San Francisco Calif USA 1970

[61] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoUnified multi-target approach for the rational insilico design of anti-bladder cancer agentsrdquo Anti-Cancer Agentsin Medicial Chemistry vol 13 no 5 pp 791ndash800 2013

[62] A Speck-Planche V V Kleandrova and M N Cordeiro ldquoNewinsights toward the discovery of antibacterial agents multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugsrdquo TheEuropean Journal of Pharmaceutical Sciences vol 48 no 4-5pp 812ndash818 2013

[63] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoMulti-target inhibitors for proteins associated withAlzheimer in silico discovery using fragment-based descrip-torsrdquo Current Alzheimer Research vol 10 no 2 pp 117ndash1242013

[64] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoIn silico discovery and virtual screening of multi-target inhibitors for proteins in Mycobacterium tuberculosisrdquoCombinatorial Chemistry amp High Throughput Screening vol 15no 8 pp 666ndash673 2012

[65] A Speck-Planche V V Kleandrova F Luan and M N DS Cordeiro ldquoChemoinformatics in anti-cancer chemotherapymulti-target QSAR model for the in silico discovery of anti-breast cancer agentsrdquo The European Journal of PharmaceuticalSciences vol 47 no 1 pp 273ndash279 2012

Submit your manuscripts athttpwwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MEDIATORSINFLAMMATION

of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Behavioural Neurology

EndocrinologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Disease Markers

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

OncologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Oxidative Medicine and Cellular Longevity

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational and Mathematical Methods in Medicine

OphthalmologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Diabetes ResearchJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Research and TreatmentAIDS

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Gastroenterology Research and Practice

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Page 13: Research Article Model for Vaccine Design by …downloads.hindawi.com/journals/jir/2014/768515.pdfResearch Article Model for Vaccine Design by Prediction of B-Epitopes of IEDB Given

Journal of Immunology Research 13

The first input term is the value 120582(120576119894119895)ref is the scoring

function 120582 of the efficiency of the initial process 120576119894119895(known

solution) The function 120582(120576119894119895)ref = 1 if the 119894th peptide could

experimentally be demonstrated to be a B-epitope in theassay of reference (reference) carried out in the conditions119888119895 120582(120576119894119895)ref = 0 otherwise The variational-perturbation

terms ΔΔ120594119888119895are at the same time terms typical of pertur-

bation theory and moving average (MA) functions used inBox-Jenkin models in time series [60] These new types ofterms account both for the deviation of the electronegativityof all amino acids in the sequence of the new peptidewith respect to the peptide of reference and with respectto all boundary conditions In Table 1 we give the overallclassification results obtainedwith thismodel Speck-Plancheet al [61ndash63] introduced different multitargetmultiplexingQSARmodels that incorporate this type of information basedon MAs The results obtained with the present model areexcellent comparedwith other similarmodels in the literatureuseful for other problems including moving average models[64 65] or perturbation models [58] Notably this is also thefirst model combining both perturbation theory and MAs ina QSPR context

The other input terms are the following The first Δ120594seq =120594(119898119902)ref minus120594(119898119894)new is the perturbation term for the variation

or in themean value of electronegativity for all amino acids inthe sequence of the peptide of reference The remnant inputvariables of the model ΔΔ120594

119888119895= Δ120594

119888119895-ref minus Δ120594119888119895-new =

[120594(119898119902)ref minus

lowast120594 (119888119902119903)ref] minus [120594(119898119894)new minus

lowast120594 (119888119894119895)new] quantify

values of the conditions of the new assay 119888119895-new that representperturbations with respect to the initial conditions 119888

119894119895-ref of

the assay of reference The quantities lowast120594(119888119894119895) and lowast120594(119888

119902119903) are

the average values of the mean electronegativity values 120594(119898119894)

and 120594(119898119902) for all new and reference peptides in IEDB that

are epitopes under the 119895th or 119903th boundary condition Thevalues of these terms have been tabulated for gt500 sourceorganisms gt50 host organisms gt10 biological process andgt30 experimental techniques We must substitute the valuesof 120594(119898

119894) and 120594(119898

119902) of the new and reference peptides and the

tabulated values of lowast120594(119888119894119895) and lowast120594(119888

119902119903) for all combinations

of boundary conditions to predict the perturbations of theaction as epitope of peptides In doing so we can found theoptimal sequence and boundary conditions towards the useof the peptide in the development of a vaccine In Table 2 wegive some of these values of lowast120594(119888

119894119895) and lowast120594(119888

119902119903)

In Table 3 we depict the sequences and input-outputboundary conditions for top perturbations present in IEDBAll these perturbations have observed value of 120582(120576

119894119895)new = 1

and predicted value also equal to 1 with a high probabilitySee Supplementary Material available online at httpdxdoiorg1011552014768515 file contains a full list of gt200000cases of perturbations

4 Conclusions

It is possible to develop generalmodels for vaccine design ableto predict the results of multiple input-output perturbations

in peptide sequence and experimental assay boundary condi-tions using ideas of QSPR analysis perturbation theory andBox and Jenkins MA operators The electronegativity valuescalculated withMARCH-INSIDE seem to be goodmoleculardescriptors for this type of QSPR-perturbation models

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

The present study was partially supported by GrantsAGL2010-22290-C02 and AGL2011-30563-C03 from Minis-terio de Ciencia e Innovacion Spain and Grant CN 2012155from Xunta de Galicia Spain

References

[1] B Peters J Sidney P Bourne et al ldquoThe design and implemen-tation of the immune epitope database and analysis resourcerdquoImmunogenetics vol 57 no 5 pp 326ndash336 2005

[2] B Peters J Sidney P Bourne et al ldquoThe immune epitopedatabase and analysis resource from vision to blueprintrdquo PLoSBiology vol 3 no 3 p e91 2005

[3] PWang A AMorgan Q Zhang A Sette and B Peters ldquoAuto-mating document classification for the Immune Epitope Data-baserdquo BMC Bioinformatics vol 8 article 269 2007

[4] A Sette ldquoThe immune epitope database and analysis resourcefrom vision to blueprintrdquo Genome Informatics vol 15 no 2 p299 2004

[5] N Salimi W Fleri B Peters and A Sette ldquoThe immune epi-tope database a historical retrospective of the first decaderdquoImmunology vol 137 no 2 pp 117ndash123 2012

[6] Y Kim A Sette and B Peters ldquoApplications for T-cell epitopequeries and tools in the Immune EpitopeDatabase andAnalysisResourcerdquo Journal of Immunological Methods vol 374 no 1-2pp 62ndash69 2011

[7] Y Kim J Ponomarenko Z Zhu D Tamang et al ldquoImmuneepitope database analysis resourcerdquo Nucleic Acids Research ppW525ndashW530 2012

[8] A M Helguera R D Combes M P Gonzalez and M N DS Cordeiro ldquoApplications of 2D descriptors in drug design aDRAGON talerdquo Current Topics in Medicinal Chemistry vol 8no 18 pp 1628ndash1655 2008

[9] GM Casanola-Martın Y Marrero-Ponce M T H Khan et alldquoDragon method for finding novel tyrosinase inhibitors Biosil-ico identification and experimental in vitro assaysrdquo The Euro-pean Journal of Medicinal Chemistry vol 42 no 11-12 pp 1370ndash1381 2007

[10] I V Tetko J Gasteiger R Todeschini et al ldquoVirtual computa-tional chemistry laboratorymdashdesign and descriptionrdquo Journalof Computer-AidedMolecular Design vol 19 no 6 pp 453ndash4632005

[11] A R Katritzky A Oliferenko A Lomaka and M KarelsonldquoSix-membered cyclic ureas as HIV-1 protease inhibitors aQSAR study based on CODESSA PRO approachrdquo Bioorganicand Medicinal Chemistry Letters vol 12 no 23 pp 3453ndash34572002

14 Journal of Immunology Research

[12] A R Katritzky S Perumal R Petrukhin and E Klein-peter ldquoCODESSA-based theoretical QSPRmodel for hydantoinHPLC-RT lipophilicitiesrdquo Journal of Chemical Information andComputer Sciences vol 41 no 3 pp 569ndash574 2001

[13] S Vilar G Cozza and S Moro ldquoMedicinal chemistry and themolecular operating environment (MOE) application of QSARand molecular docking to drug discoveryrdquo Current Topics inMedicinal Chemistry vol 8 no 18 pp 1555ndash1572 2008

[14] G Marzaro A Chilin A Guiotto et al ldquoUsing the TOPS-MODE approach to fit multi-target QSAR models for tyrosinekinases inhibitorsrdquo The European Journal of Medicinal Chem-istry vol 46 no 6 pp 2185ndash2192 2011

[15] S Vilar E Estrada E Uriarte L Santana and Y Gutierrez ldquoInsilico studies toward the discovery of new anti-HIV nucleosidecompounds through the use of tops-mode and 2D3D con-nectivity indices 2 Purine derivativesrdquo Journal of ChemicalInformation and Modeling vol 45 no 2 pp 502ndash514 2005

[16] E Estrada J A Quincoces and G Patlewicz ldquoCreating molec-ular diversity from antioxidants in Brazilian propolis combina-tion of TOPS-MODE QSAR and virtual structure generationrdquoMolecular Diversity vol 8 no 1 pp 21ndash33 2004

[17] E Estrada andHGonzalez ldquoWhat are the limits of applicabilityfor graph theoretic descriptors in QSPRQSAR modelingdipole moments of aromatic compounds with TOPS-MODEdescriptorsrdquo Journal of Chemical Information and ComputerSciences vol 43 no 1 pp 75ndash84 2003

[18] Y Marrero-Ponce J A Castillo-Garit E Olazabal et al ldquoTom-ocomd-Cardd a novel approach for computer-aided ldquorationalrdquodrug design I Theoretical and experimental assessment of apromising method for computational screening and in silicodesign of new anthelmintic compoundsrdquo Journal of Computer-Aided Molecular Design vol 18 no 10 pp 615ndash634 2004

[19] Y Marrero Ponce R Medina Marrero E A Castro et al ldquoPro-tein quadratic indices of the ldquomacromolecular pseudographrsquos 120572-carbon atom adjacency matrixrdquo 1 prediction of arc repressoralanine-mutantrsquos stabilityrdquoMolecules vol 9 no 12 pp 1124ndash11472004

[20] H Gonzalez-Dıaz F Prado-Prado and F M Ubeira ldquoPredict-ing antimicrobial drugs and targets with the MARCH-INSIDEapproachrdquo Current Topics in Medicinal Chemistry vol 8 no 18pp 1676ndash1690 2008

[21] R Garcıa-Domenech J Galvez J V de Julian-Ortiz and LPogliani ldquoSomenew trends in chemical graph theoryrdquoChemicalReviews vol 108 no 3 pp 1127ndash1169 2008

[22] E Estrada and E Uriarte ldquoRecent advances on the role of topo-logical indices in drug discovery researchrdquo Current MedicinalChemistry vol 8 no 13 pp 1573ndash1588 2001

[23] R Todeschini andVConsonniHandbook ofMolecularDescrip-tors Wiley-VCH Weinheim Germany 2008

[24] E Estrada E J Delgado J B Alderete and G A Jana ldquoQuan-tum-connectivity descriptors inmodeling solubility of environ-mentally important organic compoundsrdquo Journal of Computa-tional Chemistry vol 25 no 14 pp 1787ndash1796 2004

[25] E Besalu X Girones L Amat andR Carbo-Dorca ldquoMolecularquantum similarity and the fundamentals of QSARrdquo Accountsof Chemical Research vol 35 no 5 pp 289ndash295 2002

[26] D A Rincon M N D S Cordeiro and R A Mosquera ldquoOnthe electronic structure of cocaine and its metabolitesrdquo Journalof Physical Chemistry A vol 113 no 50 pp 13937ndash13942 2009

[27] M Mandado M J Gonzalez-Moa and R A MosqueraldquoChemical graph theory and n-center electron delocalization

indices a study on polycyclic aromatic hydrocarbonsrdquo Journalof Computational Chemistry vol 28 no 10 pp 1625ndash1633 2007

[28] THill andP Lewicki STATISTICSMethods andApplications AComprehensive Reference for Science Industry and DataMiningStatSoft Tulsa Okla USA 2006

[29] E Frank M Hall L Trigg G Holmes and I H Witten ldquoDatamining in bioinformatics using Wekardquo Bioinformatics vol 20no 15 pp 2479ndash2481 2004

[30] G Patlewicz N Ball E D Booth E Hulzebos E Zvinavasheand C Hennes ldquoUse of category approaches read-across and(Q)SAR general considerationsrdquo Regulatory Toxicology andPharmacology vol 67 no 1 pp 1ndash12 2013

[31] D W Roberts and G Y Patlewicz ldquoUpdating the skin sensiti-zation in vitro data assessment paradigm in 2009mdasha chemistryand QSAR perspectiverdquo Journal of Applied Toxicology vol 30no 3 pp 286ndash288 2010

[32] D W Roberts and G Y Patlewicz ldquoNonanimal alternatives forskin sensitization letter to the editorrdquoToxicological Sciences vol106 no 2 pp 572ndash574 2008

[33] E Estrada G Patlewicz and Y Gutierrez ldquoFrom knowledgegeneration to knowledge archive a general strategy usingTOPS-MODE with DEREK to formulate new alerts for skinsensitizationrdquo Journal of Chemical Information and ComputerSciences vol 44 no 2 pp 688ndash698 2004

[34] G F Gerberick C A Ryan P S Kern et al ldquoA chemical datasetfor evaluation of alternative approaches to skin-sensitizationtestingrdquo Contact Dermatitis vol 50 no 5 pp 274ndash288 2004

[35] G Y Patlewicz D A Basketter C K Smith Pease et al ldquoFurtherevaluation of quantitative structurendashactivity relationship mod-els for the prediction of the skin sensitization potency ofselected fragrance allergensrdquo Contact Dermatitis vol 50 no 2pp 91ndash97 2004

[36] E Estrada G Patlewicz M Chamberlain D Basketter andS Larbey ldquoComputer-aided knowledge generation for under-standing skin sensitization mechanisms the TOPS-MODEapproachrdquo Chemical Research in Toxicology vol 16 no 10 pp1226ndash1235 2003

[37] G Patlewicz R Rodford and J D Walker ldquoQuantitative struc-ture-activity relationships for predicting skin and eye irritationrdquoEnvironmental Toxicology and Chemistry vol 22 no 8 pp1862ndash1869 2003

[38] G Y Patlewicz R A Rodford G Ellis and M D Barratt ldquoAQSAR model for the eye irritation of cationic surfactantsrdquo Tox-icology In Vitro vol 14 no 1 pp 79ndash84 2000

[39] E Tenorio-Borroto C G Penuelas Rivas J C VasquezChagoyan N Castanedo F J Prado-Prado and X Garcia-Mera ldquoANN multiplexing model of drugs effect on macro-phages theoretical and flow cytometry study on the cyto-toxicity of the anti-microbial drug G1 in spleenrdquo Bioorganic ampMedicinal Chemistry vol 20 no 20 pp 6181ndash6194 2012

[40] D R Flower H McSparron M J Blythe et al ldquoComputationalvaccinology quantitative approachesrdquo Novartis FoundationSymposium vol 254 pp 102ndash120 2003

[41] I A Doytchinova P Guan and D R Flower ldquoQuantitativestructure-activity relationships and the prediction of MHCsupermotifsrdquoMethods vol 34 no 4 pp 444ndash453 2004

[42] Y Xiao and M R Segal ldquoPrediction of genomewide conservedepitope profiles of HIV-1 classifier choice and peptide rep-resentationrdquo Statistical Applications in Genetics and MolecularBiology vol 4 no 1 article 25 2005

Journal of Immunology Research 15

[43] T Fagerberg V Zoete S Viatte P Baumgaertner P M Alves PRomero et al ldquoPrediction of cross-recognition of peptide-HLAA2 by melan-a-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationshipsrdquo PLoSONE vol 8 no 7 Article ID e65590 2013

[44] D Barh A N Misra A Kumar and A Vasco ldquoA novel strategyof epitope design inNeisseria gonorrhoeaerdquo Bioinformation vol5 no 2 pp 77ndash82 2010

[45] J Bi R Song H Yang et al ldquoStepwise identification of HLA-Alowast0201-restricted CD8+ T-cell epitope peptides from herpessimplex virus type 1 genome boosted by a steprank schemerdquoBiopolymers vol 96 no 3 pp 328ndash339 2011

[46] R D Bremel and E J Homan ldquoAn integrated approach to epit-ope analysis II a system for proteomic-scale prediction ofimmunological characteristicsrdquo Immunome Research vol 6 no1 article 8 2010

[47] C M Diez-Rivero B Chenlo P Zuluaga and P A RecheldquoQuantitative modeling of peptide binding to TAP using sup-port vector machinerdquo Proteins vol 78 no 1 pp 63ndash72 2010

[48] E Martınez-Naves E M Lafuente and P A Reche ldquoRecogni-tion of the ligand-type specificity of classical and non-classicalMHC I proteinsrdquo FEBS Letters vol 585 no 21 pp 3478ndash34842011

[49] M Bhasin E L Reinherz and P A Reche ldquoRecognition andclassification of histones using support vectormachinerdquo Journalof Computational Biology vol 13 no 1 pp 102ndash112 2006

[50] P A Reche and E L Reinherz ldquoPEPVAC a web server formulti-epitope vaccine development based on the prediction ofsupertypic MHC ligandsrdquo Nucleic Acids Research vol 33 no 2pp W138ndashW142 2005

[51] E M Lafuente and P A Reche ldquoPrediction of MHC-peptidebinding a systematic and comprehensive overviewrdquo CurrentPharmaceutical Design vol 15 no 28 pp 3209ndash3220 2009

[52] A Gaulton L J Bellis A P Bento et al et al ldquoA large-scalebioactivity database for drug discoveryrdquoNucleic Acids Researchpp D1100ndashD1107 2012

[53] W H Cropper Great Physicists The Life and Times of LeadingPhysicists from Galileo to Hawking Oxford University PressNew York NY USA 2004

[54] E L Bouzarth A Brooks R Camassa et al ldquoEpicyclic orbits ina viscous fluid about a precessing rod theory and experimentsat the micro-and macro-scalesrdquo Physical Review E vol 76 no1 part 2 Article ID 016313 2007

[55] M Laberge ldquoIntrinsic protein electric fields basic non-covalentinteractions and relationship to protein-induced Stark effectsrdquoBiochimica et BiophysicaActa vol 1386 no 2 pp 305ndash330 1998

[56] FM Fernandez and J AMorales ldquoPerturbation theorywithoutwave functions for the Zeeman effect in hydrogenrdquo PhysicalReview A vol 46 no 1 pp 318ndash326 1992

[57] B D Marshall and W G Chapman ldquoThermodynamic pertur-bation theory for associating fluids with small bond angleseffects of steric hindrance ring formation and double bondingrdquoPhysical Reiew E vol 87 no 5 Article ID 052307 12 pages 2013

[58] H Gonzalez-Diaz S Arrasate A Gomez-San Juan et al ldquoNewtheory for multiple input-output perturbations in complexmolecular systems 1 Linear QSPR electronegativity models inphysical organic and medicinal chemistryrdquo Current Topics inMedicinal Chemistry 2013

[59] H Gonzalez-Diaz S Arrasate N Sotomayor et al ldquoMIANNmodels in medicinal physical and organic chemistryrdquo CurrentTopics in Medicinal Chemistry vol 13 no 5 pp 619ndash641 2013

[60] G E P Box and GM Jenkins Time Series Analysis Forecastingand Control Holden-Day San Francisco Calif USA 1970

[61] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoUnified multi-target approach for the rational insilico design of anti-bladder cancer agentsrdquo Anti-Cancer Agentsin Medicial Chemistry vol 13 no 5 pp 791ndash800 2013

[62] A Speck-Planche V V Kleandrova and M N Cordeiro ldquoNewinsights toward the discovery of antibacterial agents multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugsrdquo TheEuropean Journal of Pharmaceutical Sciences vol 48 no 4-5pp 812ndash818 2013

[63] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoMulti-target inhibitors for proteins associated withAlzheimer in silico discovery using fragment-based descrip-torsrdquo Current Alzheimer Research vol 10 no 2 pp 117ndash1242013

[64] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoIn silico discovery and virtual screening of multi-target inhibitors for proteins in Mycobacterium tuberculosisrdquoCombinatorial Chemistry amp High Throughput Screening vol 15no 8 pp 666ndash673 2012

[65] A Speck-Planche V V Kleandrova F Luan and M N DS Cordeiro ldquoChemoinformatics in anti-cancer chemotherapymulti-target QSAR model for the in silico discovery of anti-breast cancer agentsrdquo The European Journal of PharmaceuticalSciences vol 47 no 1 pp 273ndash279 2012

Submit your manuscripts athttpwwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MEDIATORSINFLAMMATION

of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Behavioural Neurology

EndocrinologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Disease Markers

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

OncologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Oxidative Medicine and Cellular Longevity

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational and Mathematical Methods in Medicine

OphthalmologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Diabetes ResearchJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Research and TreatmentAIDS

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Gastroenterology Research and Practice

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Page 14: Research Article Model for Vaccine Design by …downloads.hindawi.com/journals/jir/2014/768515.pdfResearch Article Model for Vaccine Design by Prediction of B-Epitopes of IEDB Given

14 Journal of Immunology Research

[12] A R Katritzky S Perumal R Petrukhin and E Klein-peter ldquoCODESSA-based theoretical QSPRmodel for hydantoinHPLC-RT lipophilicitiesrdquo Journal of Chemical Information andComputer Sciences vol 41 no 3 pp 569ndash574 2001

[13] S Vilar G Cozza and S Moro ldquoMedicinal chemistry and themolecular operating environment (MOE) application of QSARand molecular docking to drug discoveryrdquo Current Topics inMedicinal Chemistry vol 8 no 18 pp 1555ndash1572 2008

[14] G Marzaro A Chilin A Guiotto et al ldquoUsing the TOPS-MODE approach to fit multi-target QSAR models for tyrosinekinases inhibitorsrdquo The European Journal of Medicinal Chem-istry vol 46 no 6 pp 2185ndash2192 2011

[15] S Vilar E Estrada E Uriarte L Santana and Y Gutierrez ldquoInsilico studies toward the discovery of new anti-HIV nucleosidecompounds through the use of tops-mode and 2D3D con-nectivity indices 2 Purine derivativesrdquo Journal of ChemicalInformation and Modeling vol 45 no 2 pp 502ndash514 2005

[16] E Estrada J A Quincoces and G Patlewicz ldquoCreating molec-ular diversity from antioxidants in Brazilian propolis combina-tion of TOPS-MODE QSAR and virtual structure generationrdquoMolecular Diversity vol 8 no 1 pp 21ndash33 2004

[17] E Estrada andHGonzalez ldquoWhat are the limits of applicabilityfor graph theoretic descriptors in QSPRQSAR modelingdipole moments of aromatic compounds with TOPS-MODEdescriptorsrdquo Journal of Chemical Information and ComputerSciences vol 43 no 1 pp 75ndash84 2003

[18] Y Marrero-Ponce J A Castillo-Garit E Olazabal et al ldquoTom-ocomd-Cardd a novel approach for computer-aided ldquorationalrdquodrug design I Theoretical and experimental assessment of apromising method for computational screening and in silicodesign of new anthelmintic compoundsrdquo Journal of Computer-Aided Molecular Design vol 18 no 10 pp 615ndash634 2004

[19] Y Marrero Ponce R Medina Marrero E A Castro et al ldquoPro-tein quadratic indices of the ldquomacromolecular pseudographrsquos 120572-carbon atom adjacency matrixrdquo 1 prediction of arc repressoralanine-mutantrsquos stabilityrdquoMolecules vol 9 no 12 pp 1124ndash11472004

[20] H Gonzalez-Dıaz F Prado-Prado and F M Ubeira ldquoPredict-ing antimicrobial drugs and targets with the MARCH-INSIDEapproachrdquo Current Topics in Medicinal Chemistry vol 8 no 18pp 1676ndash1690 2008

[21] R Garcıa-Domenech J Galvez J V de Julian-Ortiz and LPogliani ldquoSomenew trends in chemical graph theoryrdquoChemicalReviews vol 108 no 3 pp 1127ndash1169 2008

[22] E Estrada and E Uriarte ldquoRecent advances on the role of topo-logical indices in drug discovery researchrdquo Current MedicinalChemistry vol 8 no 13 pp 1573ndash1588 2001

[23] R Todeschini andVConsonniHandbook ofMolecularDescrip-tors Wiley-VCH Weinheim Germany 2008

[24] E Estrada E J Delgado J B Alderete and G A Jana ldquoQuan-tum-connectivity descriptors inmodeling solubility of environ-mentally important organic compoundsrdquo Journal of Computa-tional Chemistry vol 25 no 14 pp 1787ndash1796 2004

[25] E Besalu X Girones L Amat andR Carbo-Dorca ldquoMolecularquantum similarity and the fundamentals of QSARrdquo Accountsof Chemical Research vol 35 no 5 pp 289ndash295 2002

[26] D A Rincon M N D S Cordeiro and R A Mosquera ldquoOnthe electronic structure of cocaine and its metabolitesrdquo Journalof Physical Chemistry A vol 113 no 50 pp 13937ndash13942 2009

[27] M Mandado M J Gonzalez-Moa and R A MosqueraldquoChemical graph theory and n-center electron delocalization

indices a study on polycyclic aromatic hydrocarbonsrdquo Journalof Computational Chemistry vol 28 no 10 pp 1625ndash1633 2007

[28] THill andP Lewicki STATISTICSMethods andApplications AComprehensive Reference for Science Industry and DataMiningStatSoft Tulsa Okla USA 2006

[29] E Frank M Hall L Trigg G Holmes and I H Witten ldquoDatamining in bioinformatics using Wekardquo Bioinformatics vol 20no 15 pp 2479ndash2481 2004

[30] G Patlewicz N Ball E D Booth E Hulzebos E Zvinavasheand C Hennes ldquoUse of category approaches read-across and(Q)SAR general considerationsrdquo Regulatory Toxicology andPharmacology vol 67 no 1 pp 1ndash12 2013

[31] D W Roberts and G Y Patlewicz ldquoUpdating the skin sensiti-zation in vitro data assessment paradigm in 2009mdasha chemistryand QSAR perspectiverdquo Journal of Applied Toxicology vol 30no 3 pp 286ndash288 2010

[32] D W Roberts and G Y Patlewicz ldquoNonanimal alternatives forskin sensitization letter to the editorrdquoToxicological Sciences vol106 no 2 pp 572ndash574 2008

[33] E Estrada G Patlewicz and Y Gutierrez ldquoFrom knowledgegeneration to knowledge archive a general strategy usingTOPS-MODE with DEREK to formulate new alerts for skinsensitizationrdquo Journal of Chemical Information and ComputerSciences vol 44 no 2 pp 688ndash698 2004

[34] G F Gerberick C A Ryan P S Kern et al ldquoA chemical datasetfor evaluation of alternative approaches to skin-sensitizationtestingrdquo Contact Dermatitis vol 50 no 5 pp 274ndash288 2004

[35] G Y Patlewicz D A Basketter C K Smith Pease et al ldquoFurtherevaluation of quantitative structurendashactivity relationship mod-els for the prediction of the skin sensitization potency ofselected fragrance allergensrdquo Contact Dermatitis vol 50 no 2pp 91ndash97 2004

[36] E Estrada G Patlewicz M Chamberlain D Basketter andS Larbey ldquoComputer-aided knowledge generation for under-standing skin sensitization mechanisms the TOPS-MODEapproachrdquo Chemical Research in Toxicology vol 16 no 10 pp1226ndash1235 2003

[37] G Patlewicz R Rodford and J D Walker ldquoQuantitative struc-ture-activity relationships for predicting skin and eye irritationrdquoEnvironmental Toxicology and Chemistry vol 22 no 8 pp1862ndash1869 2003

[38] G Y Patlewicz R A Rodford G Ellis and M D Barratt ldquoAQSAR model for the eye irritation of cationic surfactantsrdquo Tox-icology In Vitro vol 14 no 1 pp 79ndash84 2000

[39] E Tenorio-Borroto C G Penuelas Rivas J C VasquezChagoyan N Castanedo F J Prado-Prado and X Garcia-Mera ldquoANN multiplexing model of drugs effect on macro-phages theoretical and flow cytometry study on the cyto-toxicity of the anti-microbial drug G1 in spleenrdquo Bioorganic ampMedicinal Chemistry vol 20 no 20 pp 6181ndash6194 2012

[40] D R Flower H McSparron M J Blythe et al ldquoComputationalvaccinology quantitative approachesrdquo Novartis FoundationSymposium vol 254 pp 102ndash120 2003

[41] I A Doytchinova P Guan and D R Flower ldquoQuantitativestructure-activity relationships and the prediction of MHCsupermotifsrdquoMethods vol 34 no 4 pp 444ndash453 2004

[42] Y Xiao and M R Segal ldquoPrediction of genomewide conservedepitope profiles of HIV-1 classifier choice and peptide rep-resentationrdquo Statistical Applications in Genetics and MolecularBiology vol 4 no 1 article 25 2005

Journal of Immunology Research 15

[43] T Fagerberg V Zoete S Viatte P Baumgaertner P M Alves PRomero et al ldquoPrediction of cross-recognition of peptide-HLAA2 by melan-a-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationshipsrdquo PLoSONE vol 8 no 7 Article ID e65590 2013

[44] D Barh A N Misra A Kumar and A Vasco ldquoA novel strategyof epitope design inNeisseria gonorrhoeaerdquo Bioinformation vol5 no 2 pp 77ndash82 2010

[45] J Bi R Song H Yang et al ldquoStepwise identification of HLA-Alowast0201-restricted CD8+ T-cell epitope peptides from herpessimplex virus type 1 genome boosted by a steprank schemerdquoBiopolymers vol 96 no 3 pp 328ndash339 2011

[46] R D Bremel and E J Homan ldquoAn integrated approach to epit-ope analysis II a system for proteomic-scale prediction ofimmunological characteristicsrdquo Immunome Research vol 6 no1 article 8 2010

[47] C M Diez-Rivero B Chenlo P Zuluaga and P A RecheldquoQuantitative modeling of peptide binding to TAP using sup-port vector machinerdquo Proteins vol 78 no 1 pp 63ndash72 2010

[48] E Martınez-Naves E M Lafuente and P A Reche ldquoRecogni-tion of the ligand-type specificity of classical and non-classicalMHC I proteinsrdquo FEBS Letters vol 585 no 21 pp 3478ndash34842011

[49] M Bhasin E L Reinherz and P A Reche ldquoRecognition andclassification of histones using support vectormachinerdquo Journalof Computational Biology vol 13 no 1 pp 102ndash112 2006

[50] P A Reche and E L Reinherz ldquoPEPVAC a web server formulti-epitope vaccine development based on the prediction ofsupertypic MHC ligandsrdquo Nucleic Acids Research vol 33 no 2pp W138ndashW142 2005

[51] E M Lafuente and P A Reche ldquoPrediction of MHC-peptidebinding a systematic and comprehensive overviewrdquo CurrentPharmaceutical Design vol 15 no 28 pp 3209ndash3220 2009

[52] A Gaulton L J Bellis A P Bento et al et al ldquoA large-scalebioactivity database for drug discoveryrdquoNucleic Acids Researchpp D1100ndashD1107 2012

[53] W H Cropper Great Physicists The Life and Times of LeadingPhysicists from Galileo to Hawking Oxford University PressNew York NY USA 2004

[54] E L Bouzarth A Brooks R Camassa et al ldquoEpicyclic orbits ina viscous fluid about a precessing rod theory and experimentsat the micro-and macro-scalesrdquo Physical Review E vol 76 no1 part 2 Article ID 016313 2007

[55] M Laberge ldquoIntrinsic protein electric fields basic non-covalentinteractions and relationship to protein-induced Stark effectsrdquoBiochimica et BiophysicaActa vol 1386 no 2 pp 305ndash330 1998

[56] FM Fernandez and J AMorales ldquoPerturbation theorywithoutwave functions for the Zeeman effect in hydrogenrdquo PhysicalReview A vol 46 no 1 pp 318ndash326 1992

[57] B D Marshall and W G Chapman ldquoThermodynamic pertur-bation theory for associating fluids with small bond angleseffects of steric hindrance ring formation and double bondingrdquoPhysical Reiew E vol 87 no 5 Article ID 052307 12 pages 2013

[58] H Gonzalez-Diaz S Arrasate A Gomez-San Juan et al ldquoNewtheory for multiple input-output perturbations in complexmolecular systems 1 Linear QSPR electronegativity models inphysical organic and medicinal chemistryrdquo Current Topics inMedicinal Chemistry 2013

[59] H Gonzalez-Diaz S Arrasate N Sotomayor et al ldquoMIANNmodels in medicinal physical and organic chemistryrdquo CurrentTopics in Medicinal Chemistry vol 13 no 5 pp 619ndash641 2013

[60] G E P Box and GM Jenkins Time Series Analysis Forecastingand Control Holden-Day San Francisco Calif USA 1970

[61] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoUnified multi-target approach for the rational insilico design of anti-bladder cancer agentsrdquo Anti-Cancer Agentsin Medicial Chemistry vol 13 no 5 pp 791ndash800 2013

[62] A Speck-Planche V V Kleandrova and M N Cordeiro ldquoNewinsights toward the discovery of antibacterial agents multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugsrdquo TheEuropean Journal of Pharmaceutical Sciences vol 48 no 4-5pp 812ndash818 2013

[63] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoMulti-target inhibitors for proteins associated withAlzheimer in silico discovery using fragment-based descrip-torsrdquo Current Alzheimer Research vol 10 no 2 pp 117ndash1242013

[64] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoIn silico discovery and virtual screening of multi-target inhibitors for proteins in Mycobacterium tuberculosisrdquoCombinatorial Chemistry amp High Throughput Screening vol 15no 8 pp 666ndash673 2012

[65] A Speck-Planche V V Kleandrova F Luan and M N DS Cordeiro ldquoChemoinformatics in anti-cancer chemotherapymulti-target QSAR model for the in silico discovery of anti-breast cancer agentsrdquo The European Journal of PharmaceuticalSciences vol 47 no 1 pp 273ndash279 2012

Submit your manuscripts athttpwwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MEDIATORSINFLAMMATION

of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Behavioural Neurology

EndocrinologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Disease Markers

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

OncologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Oxidative Medicine and Cellular Longevity

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational and Mathematical Methods in Medicine

OphthalmologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Diabetes ResearchJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Research and TreatmentAIDS

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Gastroenterology Research and Practice

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Page 15: Research Article Model for Vaccine Design by …downloads.hindawi.com/journals/jir/2014/768515.pdfResearch Article Model for Vaccine Design by Prediction of B-Epitopes of IEDB Given

Journal of Immunology Research 15

[43] T Fagerberg V Zoete S Viatte P Baumgaertner P M Alves PRomero et al ldquoPrediction of cross-recognition of peptide-HLAA2 by melan-a-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationshipsrdquo PLoSONE vol 8 no 7 Article ID e65590 2013

[44] D Barh A N Misra A Kumar and A Vasco ldquoA novel strategyof epitope design inNeisseria gonorrhoeaerdquo Bioinformation vol5 no 2 pp 77ndash82 2010

[45] J Bi R Song H Yang et al ldquoStepwise identification of HLA-Alowast0201-restricted CD8+ T-cell epitope peptides from herpessimplex virus type 1 genome boosted by a steprank schemerdquoBiopolymers vol 96 no 3 pp 328ndash339 2011

[46] R D Bremel and E J Homan ldquoAn integrated approach to epit-ope analysis II a system for proteomic-scale prediction ofimmunological characteristicsrdquo Immunome Research vol 6 no1 article 8 2010

[47] C M Diez-Rivero B Chenlo P Zuluaga and P A RecheldquoQuantitative modeling of peptide binding to TAP using sup-port vector machinerdquo Proteins vol 78 no 1 pp 63ndash72 2010

[48] E Martınez-Naves E M Lafuente and P A Reche ldquoRecogni-tion of the ligand-type specificity of classical and non-classicalMHC I proteinsrdquo FEBS Letters vol 585 no 21 pp 3478ndash34842011

[49] M Bhasin E L Reinherz and P A Reche ldquoRecognition andclassification of histones using support vectormachinerdquo Journalof Computational Biology vol 13 no 1 pp 102ndash112 2006

[50] P A Reche and E L Reinherz ldquoPEPVAC a web server formulti-epitope vaccine development based on the prediction ofsupertypic MHC ligandsrdquo Nucleic Acids Research vol 33 no 2pp W138ndashW142 2005

[51] E M Lafuente and P A Reche ldquoPrediction of MHC-peptidebinding a systematic and comprehensive overviewrdquo CurrentPharmaceutical Design vol 15 no 28 pp 3209ndash3220 2009

[52] A Gaulton L J Bellis A P Bento et al et al ldquoA large-scalebioactivity database for drug discoveryrdquoNucleic Acids Researchpp D1100ndashD1107 2012

[53] W H Cropper Great Physicists The Life and Times of LeadingPhysicists from Galileo to Hawking Oxford University PressNew York NY USA 2004

[54] E L Bouzarth A Brooks R Camassa et al ldquoEpicyclic orbits ina viscous fluid about a precessing rod theory and experimentsat the micro-and macro-scalesrdquo Physical Review E vol 76 no1 part 2 Article ID 016313 2007

[55] M Laberge ldquoIntrinsic protein electric fields basic non-covalentinteractions and relationship to protein-induced Stark effectsrdquoBiochimica et BiophysicaActa vol 1386 no 2 pp 305ndash330 1998

[56] FM Fernandez and J AMorales ldquoPerturbation theorywithoutwave functions for the Zeeman effect in hydrogenrdquo PhysicalReview A vol 46 no 1 pp 318ndash326 1992

[57] B D Marshall and W G Chapman ldquoThermodynamic pertur-bation theory for associating fluids with small bond angleseffects of steric hindrance ring formation and double bondingrdquoPhysical Reiew E vol 87 no 5 Article ID 052307 12 pages 2013

[58] H Gonzalez-Diaz S Arrasate A Gomez-San Juan et al ldquoNewtheory for multiple input-output perturbations in complexmolecular systems 1 Linear QSPR electronegativity models inphysical organic and medicinal chemistryrdquo Current Topics inMedicinal Chemistry 2013

[59] H Gonzalez-Diaz S Arrasate N Sotomayor et al ldquoMIANNmodels in medicinal physical and organic chemistryrdquo CurrentTopics in Medicinal Chemistry vol 13 no 5 pp 619ndash641 2013

[60] G E P Box and GM Jenkins Time Series Analysis Forecastingand Control Holden-Day San Francisco Calif USA 1970

[61] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoUnified multi-target approach for the rational insilico design of anti-bladder cancer agentsrdquo Anti-Cancer Agentsin Medicial Chemistry vol 13 no 5 pp 791ndash800 2013

[62] A Speck-Planche V V Kleandrova and M N Cordeiro ldquoNewinsights toward the discovery of antibacterial agents multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugsrdquo TheEuropean Journal of Pharmaceutical Sciences vol 48 no 4-5pp 812ndash818 2013

[63] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoMulti-target inhibitors for proteins associated withAlzheimer in silico discovery using fragment-based descrip-torsrdquo Current Alzheimer Research vol 10 no 2 pp 117ndash1242013

[64] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoIn silico discovery and virtual screening of multi-target inhibitors for proteins in Mycobacterium tuberculosisrdquoCombinatorial Chemistry amp High Throughput Screening vol 15no 8 pp 666ndash673 2012

[65] A Speck-Planche V V Kleandrova F Luan and M N DS Cordeiro ldquoChemoinformatics in anti-cancer chemotherapymulti-target QSAR model for the in silico discovery of anti-breast cancer agentsrdquo The European Journal of PharmaceuticalSciences vol 47 no 1 pp 273ndash279 2012

Submit your manuscripts athttpwwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MEDIATORSINFLAMMATION

of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Behavioural Neurology

EndocrinologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Disease Markers

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

OncologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Oxidative Medicine and Cellular Longevity

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational and Mathematical Methods in Medicine

OphthalmologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Diabetes ResearchJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Research and TreatmentAIDS

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Gastroenterology Research and Practice

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Page 16: Research Article Model for Vaccine Design by …downloads.hindawi.com/journals/jir/2014/768515.pdfResearch Article Model for Vaccine Design by Prediction of B-Epitopes of IEDB Given

Submit your manuscripts athttpwwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MEDIATORSINFLAMMATION

of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Behavioural Neurology

EndocrinologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Disease Markers

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

OncologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Oxidative Medicine and Cellular Longevity

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational and Mathematical Methods in Medicine

OphthalmologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Diabetes ResearchJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Research and TreatmentAIDS

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Gastroenterology Research and Practice

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom