ME T H O D S I N MO L E C U L A R B I O L O G Y

Series EditorJohn M. Walker

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For over 35 years, biological scientists have come to rely on the research protocols andmethodologies in the critically acclaimedMethods in Molecular Biology series. The series wasthe first to introduce the step-by-step protocols approach that has become the standard in allbiomedical protocol publishing. Each protocol is provided in readily-reproducible step-by-step fashion, opening with an introductory overview, a list of the materials and reagentsneeded to complete the experiment, and followed by a detailed procedure that is supportedwith a helpful notes section offering tips and tricks of the trade as well as troubleshootingadvice. These hallmark features were introduced by series editor Dr. John Walker andconstitute the key ingredient in each and every volume of the Methods in Molecular Biologyseries. Tested and trusted, comprehensive and reliable, all protocols from the series areindexed in PubMed.

Immunoinformatics

Third Edition

Edited by

Namrata Tomar

Department of BioMedical Engineering, Medical College of Wisconsin, Milwaukee, WI, USA

EditorNamrata TomarDepartment of BioMedical EngineeringMedical College of WisconsinMilwaukee, WI, USA

ISSN 1064-3745 ISSN 1940-6029 (electronic)Methods in Molecular BiologyISBN 978-1-0716-0388-8 ISBN 978-1-0716-0389-5 (eBook)https://doi.org/10.1007/978-1-0716-0389-5

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Preface

The immune system is very complex and consists of numerous cell types, molecular path-ways, and signals, which help a host system to distinguish between normal, healthy cells andunhealthy cells. All immune cell types have a specific role and ways of recognizing potentiallyharmful foreign bodies. To get the diverse details of an immune network, a researcher mayoptimize immune responses for a specific issue that may range from minor infections tocancer. This requires implementing data mining, statistics, and machine learning approachesto convert high-throughput immune data into meaningful insights. In simpler terms,Immunoinformatics incorporates the application of bioinformatics methods, mathematicalmodels, and statistical techniques for the study of immune systems biology. The develop-ment of immunoinformatics tools, databases, and models involves computer scientists andmodeling experts working closely with immunologists in a multidisciplinary team.Modelingand computational approaches have been widely applied to solve the problems in immunol-ogy as in quantifying the data generated in laboratory experiments and extracting meaning-ful biological information on its kinetics. To state the value of computational tools andmodels in immunology research, we need a variety of immune system-related databases,prediction software and modeling tools, informatics, and computational infrastructure forconnecting computer modeling and wet-lab experimentation, as well as data analytics andvisualization.

This book consists of 23 chapters that cover diverse immunoinformatics research topics.It involves tools and databases of potential epitope prediction, HLA gene analysis, MHCcharacterizing, in silico vaccine design, mathematical modeling of host-pathogen interac-tions, and network analysis of immune system data.

Content and General Outline of the Book

Chapter 1 introduces a reverse vaccinology approach and its advantages and applications. Itbasically searches through genomic sequences to predict antigens that have a capacity to beused as potential vaccine candidates. It describes required web tools, databases, and softwareto predict potential epitopes for vaccine development.

Chapter 2 introduces a peptide-based vaccine approach to design an in silico vaccineagainst Zika virus.

Chapter 3 focuses on high-definition genomic analysis of human leukocyte antigen(HLA) genes that encode for major histocompatibility complex (MHC) proteins. Thegenotyping of HLA alleles was done through whole genome sequencing data, wholeexome sequencing data, or targeting sequence of HLA genes by using next-generationsequencing technology.

Chapter 4 describes detailed steps for a computational vaccine design for MERS-CoVinfections. It mostly makes use of IEDB software to predict the suitable MERS-CoVepitopevaccine.

Chapter 5 introduces an alignment-independent platform for allergenicity prediction. Itutilizes three modular servers to assess the allergenicity of a randomly selected allergenicprotein and demonstrates a protocol for fast and reliable in silico prediction.

v

Chapter 6 reviews the methodology used for computational identification of B and Tcell epitopes against enterotoxigenic Escherichia coli, along with other databases of epitopesand analysis tools for T cell and B cell epitope prediction and vaccine design.

Chapter 7 introduces immunoinformatics and molecular docking methods to screenpotential vaccine candidates for Leptospira, which is responsible for Leptospirosis, a zoo-notic disease.

Chapter 8 takes into account a residue-centric presentation score for both mutatedresidues and MHC-I genotype and hypothesized that high scores (corresponds to poorpresentation) would correlate to high mutation frequencies within tumors. To explain,MHC class I proteins present on the cell’s surface recognize tumor-specific neoantigens ofearly neoplastic cells and eliminate them before the tumor develops.

Chapter 9 suggests the importance of the network analysis of large-scale data and itsapplication in the field of immunological research. Network analysis is a way to extractcomplex information from high-throughput data and develop advanced algorithms tounveil the underlying mechanisms. This chapter discusses the ways to integrate and analyzenetworks using genome-wide transcriptional profiles.

Chapter 10 discusses the implementation of in silico tools using a multiparametricapproach to screen both B and T cell epitopes, along with a ranking system to shortlistpotential mimotope candidates to be used as peptide cancer vaccine candidates.

Chapter 11 introduces immunoinformatics approaches, e.g., epitope prediction tools,molecular docking, and population coverage analysis to design desired immunogenic pep-tides. This chapter uses these approaches to select potential peptide containing multiple T(CD8+ and CD4+) and B cell epitopes from Avian H3N2 M1 protein.

Chapter 12 focuses on monoclonal antibody (mAb) formulations, where protein-protein interfaces formed by mAb aggregation could be selectively recognized by shortpeptides with random amino acid sequences. These aggregated mAb are used to screen aphage display peptide library to pick peptides that can recognize mAb aggregates.

Chapter 13 details the protocol and provides software to build variability-free pro-teomes for epitope vaccine design implemented for human herpesvirus 1 (HHV1) andinvolves the identification of protein clusters, followed by multiple sequence alignmentsand Shannon variability calculations.

Chapter 14 utilizes the immunoinformatics tools to identify immunodominant epitopesfor Shigella flexneri and validates them through an in vivo model.

Chapter 15 overviews immunoinformatics tools and their application in in silico vaccinedesign against viral diseases.

Chapter 16 presents two online servers, EPCES and EPSVR, for discontinuous epitopeprediction for which all methods were benchmarked by a curated independent test set. Hereall antigens had no complex structures with the antibody, and their epitopes were identifiedby various biochemical experiments.

Chapter 17 introduces SVMTrip that utilizes the Support Vector Machine by combin-ing the tri-peptide similarity and propensity scores to predict B cell epitopes. It wasimplemented on non-redundant B cell linear epitopes extracted from IEDB, and it achieveda sensitivity of 80.1% and a precision of 55.2% with a fivefold cross-validation.

Chapter 18 introduces the usage of mathematical modeling in the immunoinformaticsdomain to modeling phage-bacteria dynamics to study the dynamics of this interaction.

Chapter 19 focuses on the utilization of simulation techniques in order to understandmycobacteriophage and host interactions. Mycobacterium sp. exhibits complex evolution of

vi Preface

antimicrobial resistance. Phage treatment using phage-encoded products can be usedinstead of directly using the bacteriophages (scavengers of bacteria).

Chapter 20 describes electrochemiluminescence immunoassays that are based on theprinciple of light emission in a chemical environment to detect and analyze different proteinsand biomolecules. It uses the Mesoscale Discovery System with optimization protocols todiscover more biologically relevant markers.

Chapter 21 presents the database AAgAtlas 1.0 that mines PubMed to support basic andtranslational studies associated with autoimmunity. It focuses on autoantibodies that workagainst host self-proteins and play significant roles in homeostasis maintenance and also leadto autoimmune disorders.

Chapter 22 presents different ensemble meta-learning approaches for epitope predic-tion based on stacked generalization, cascade generalization, and meta-decision trees. Themeta-learning approach enables the integration of multiple prediction models and therebyoutperforms the single best-performing model. Also, it provides a flexibility to researchersto construct various meta-classification hierarchies for epitope prediction in different proteindomains.

Chapter 23 presents a server PCPS for predicting cleavage sites generated by both theconstitutive proteasome and the immunoproteasome. PCPS is implemented for free publicuse online at http://imed.med.ucm.es/pcps/.

Milwaukee, WI, USA Namrata Tomar

Preface vii

Contents

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vContributors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi

1 Reverse Vaccinology and Its Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1Amol M. Kanampalliwar

2 Computational Methodology for Peptide Vaccine Designfor Zika Virus: A Bioinformatics Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17Ashesh Nandy, Smarajit Manna, and Subhash C. Basak

3 High-Definition Genomic Analysis of HLA Genes ViaComprehensive HLA Allele Genotyping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31Shuji Kawaguchi and Fumihiko Matsuda

4 A Computational Vaccine Designing Approachfor MERS-CoV Infections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39Hiba Siddig Ibrahim and Shamsoun Khamis Kafi

5 An Alignment-Independent Platform for Allergenicity Prediction . . . . . . . . . . . . . 147Ivan Dimitrov and Irini Doytchinova

6 Immunoinformatics and Epitope Prediction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155Jayashree Ramana and Kusum Mehla

7 Vaccine Design Against Leptospirosis Using an ImmunoinformaticApproach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173Kumari Snehkant Lata, Vibhisha Vaghasia, Shivarudrappa Bhairappanvar,Saumya Patel, and Jayashankar Das

8 Characterizing MHC-I Genotype Predictive Power for OncogenicMutation Probability in Cancer Patients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185Lainie Beauchemin, Michael Slifker, David Rossell,and Joan Font-Burgada

9 Network Analysis of Large-Scale Data and Its Applicationto Immunology. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199Lauren Benoodt and Juilee Thakar

10 In Silico-Guided Sequence Modification of Epitopes in CancerVaccine Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213Winfrey Pui Yee Hoo, Pui Yan Siak, and Lionel L. A. In

11 An Immunoinformatics Approach in Design of SyntheticPeptide Vaccine Against Influenza Virus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229Neha Lohia and Manoj Baranwal

12 A New Approach to Assess mAb Aggregation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245Illarion V. Turko

13 Generation of Variability-Free Reference Proteomesfrom Pathogenic Organisms for Epitope-Vaccine Design . . . . . . . . . . . . . . . . . . . . 255Jose L. Sanchez-Trincado and Pedro A. Reche

ix

14 Immunoinformatic Identification of Potential Epitopes. . . . . . . . . . . . . . . . . . . . . . 265Priti Desai, Divya Tarwadi, Bhargav Pandya, and Bhrugu Yagnik

15 Immunoinformatic Approaches for Vaccine DesigningAgainst Viral Infections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277Richa Anand and Richa Raghuwanshi

16 EPCES and EPSVR: Prediction of B-Cell Antigenic Epitopes onProtein Surfaces with Conformational Information . . . . . . . . . . . . . . . . . . . . . . . . . 289Shide Liang, Dandan Zheng, Bo Yao, and Chi Zhang

17 SVMTriP: A Method to Predict B-Cell Linear Antigenic Epitopes . . . . . . . . . . . . 299Bo Yao, Dandan Zheng, Shide Liang, and Chi Zhang

18 Modeling Phage–Bacteria Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309Saptarshi Sinha, Rajdeep Kaur Grewal, and Soumen Roy

19 Dynamics of Mycobacteriophage—Mycobacterial Host Interaction . . . . . . . . . . . 329Arabinda Ghosh, Tridip Phukan, Surabhi Johari, Ashwani Sharma,Abha Vashista, and Subrata Sinha

20 Multiplexing of Immune Markers via ElectrochemiluminescenceImmunoassays for Systems Biology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 349Vrushali Abhyankar and Ammaar H. Abidi

21 AAgAtlas 1.0: A Database of Human Autoantigens Extractedfrom Biomedical Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365Dan Wang, Yupeng Zhang, Qing Meng, and Xiaobo Yu

22 Application of Meta Learning to B-Cell ConformationalEpitope Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375Yuh-Jyh Hu

23 PCPS: A Web Server to Predict Proteasomal Cleavage Sites . . . . . . . . . . . . . . . . . . 399Marta Gomez-Perosanz, Alvaro Ras-Carmona, and Pedro A. Reche

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407

x Contents

Contributors

VRUSHALI ABHYANKAR • American Academy of Periodontology, UTHSC, College of Dentistry,Memphis, TN, USA

AMMAAR H. ABIDI • Department of Bioscience Research and General Dentistry, College ofDentistry, University of Tennessee Health Science Center, Memphis, TN, USA

RICHA ANAND • Department of Applied Sciences, Indian Institute of Information Technology,Allahabad, UP, India

MANOJ BARANWAL • Department of Biotechnology, Thapar Institute of Engineering andTechnology, Patiala, India

SUBHASH C. BASAK • Department of Chemistry and Biochemistry, University of Minnesota,Duluth, MN, USA

LAINIE BEAUCHEMIN • Cancer Biology Program, Fox Chase Cancer Center, Philadelphia, PA,USA

LAUREN BENOODT • Department of Biochemistry and Biophysics, University of RochesterMedical Center, Rochester, NY, USA

SHIVARUDRAPPA BHAIRAPPANVAR • Gujarat Biotechnology Research Centre, Department ofScience and Technology, Government of Gujarat, Gandhinagar, India

JAYASHANKAR DAS • Gujarat Biotechnology Research Centre, Department of Science andTechnology, Government of Gujarat, Gandhinagar, India

PRITI DESAI • Department of Biotechnology and Biological Sciences, Institute of AdvancedResearch (IAR), Gandhinagar, Gujarat, India

IVAN DIMITROV • Faculty of Pharmacy, Medical University of Sofia, Sofia, BulgariaIRINI DOYTCHINOVA • Faculty of Pharmacy, Medical University of Sofia, Sofia, BulgariaJOAN FONT-BURGADA • Cancer Biology Program, Fox Chase Cancer Center, Philadelphia,

PA, USAARABINDA GHOSH • Microbiology Division, Department of Botany, Gauhati University,

Guwahati, Assam, IndiaMARTA GOMEZ-PEROSANZ • Department of Immunology, School of Medicine, Complutense

University of Madrid, Madrid, SpainRAJDEEP KAUR GREWAL • Department of Physics, Bose Institute, Kolkata, IndiaWINFREY PUI YEE HOO • Department of Biotechnology, Faculty of Applied Sciences, UCSI

University, Kuala Lumpur, MalaysiaYUH-JYH HU • College of Computer Science, National Chiao Tung University, Hsinchu,

Taiwan; Institute of Biomedical Engineering, National Chiao Tung University, Hsinchu,Taiwan

HIBA SIDDIG IBRAHIM • Sudan Diabetic Childhood Center, Khartoum, SudanLIONEL L. A. IN • Department of Biotechnology, Faculty of Applied Sciences, UCSI

University, Kuala Lumpur, MalaysiaSURABHI JOHARI • Institute of Management Studies (IMSUC), Ghaziabad, Uttar Pradesh,

IndiaSHAMSOUN KHAMIS KAFI • Faculty of Medical Laboratory Science (MLS), The National Ribat

University, Khartoum, SudanAMOL M. KANAMPALLIWAR • Master of Technology, School of Biotechnology, UTD, Rajiv

Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India

xi

SHUJI KAWAGUCHI • Center for Genomic Medicine, Kyoto University Graduate School ofMedicine, Kyoto, Japan

KUMARI SNEHKANT LATA • Gujarat Biotechnology Research Centre, Department of Scienceand Technology, Government of Gujarat, Gandhinagar, India; Department of Botany,Bioinformatics and Climate Change, Gujarat University, Ahmedabad, India

SHIDE LIANG • Department of R&D, Bio-Thera Solutions, Guangzhou, ChinaNEHA LOHIA • Department of Biotechnology, Thapar Institute of Engineering and

Technology, Patiala, India; School of Life Sciences, Jaipur National University, Jaipur,India

SMARAJIT MANNA • Centre for Interdisciplinary Research and Education, Kolkata, India;Jagadis Bose National Science Talent Search, Kolkata, India

FUMIHIKO MATSUDA • Center for Genomic Medicine, Kyoto University Graduate School ofMedicine, Kyoto, Japan

KUSUM MEHLA • National Bureau of Animal Genetic Resources, Karnal, Haryana, IndiaQING MENG • State Key Laboratory of Proteomics, Beijing Proteome Research Center,

National Center for Protein Sciences-Beijing (PHOENIX Center), Beijing Institute ofLifeomics, Beijing, China

ASHESH NANDY • Centre for Interdisciplinary Research and Education, Kolkata, IndiaBHARGAV PANDYA • Department of Biotechnology and Biological Sciences, Institute of

Advanced Research (IAR), Gandhinagar, Gujarat, IndiaSAUMYA PATEL • Department of Botany, Bioinformatics and Climate Change, Gujarat

University, Ahmedabad, IndiaTRIDIP PHUKAN • Microbiology Division, Department of Botany, Gauhati University,

Guwahati, Assam, IndiaRICHA RAGHUWANSHI • Department of Botany, Mahila Mahavidyalaya, Banaras Hindu

University, Varanasi, UP, IndiaJAYASHREE RAMANA • Department of Biotechnology and Bioinformatics, Jaypee University of

Information Technology, Waknaghat, HP, IndiaALVARO RAS-CARMONA • Department of Immunology, School of Medicine, Complutense

University of Madrid, Madrid, SpainPEDRO A. RECHE • Department of Immunology, School of Medicine, Complutense University

of Madrid, Madrid, SpainDAVID ROSSELL • Department of Economics and Business, Universitat Pompeu Fabra,

Barcelona, SpainSOUMEN ROY • Department of Physics, Bose Institute, Kolkata, IndiaJOSE L. SANCHEZ-TRINCADO • Department of Immunology, School of Medicine, Complutense

University of Madrid, Madrid, SpainASHWANI SHARMA • Biopredic International, Parc d’activite de la Breteche Batiment A4,

Saint Gregoire, FrancePUI YAN SIAK • Department of Biotechnology, Faculty of Applied Sciences, UCSI University,

Kuala Lumpur, MalaysiaSAPTARSHI SINHA • Department of Physics, Bose Institute, Kolkata, IndiaSUBRATA SINHA • Centre for Biotechnology and Bioinformatics, Dibrugarh University,

Dibrugarh, Assam, IndiaMICHAEL SLIFKER • Cancer Biology Program, Fox Chase Cancer Center, Philadelphia, PA,

USADIVYA TARWADI • Department of Biotechnology and Biological Sciences, Institute of Advanced

Research (IAR), Gandhinagar, Gujarat, India

xii Contributors

JUILEE THAKAR • Department of Microbiology and Immunology, University of RochesterMedical Center, Rochester, NY, USA; Department of Biostatistics and ComputationalBiology, University of Rochester Medical Center, Rochester, NY, USA

ILLARION V. TURKO • Biomolecular Mesurement Division, National Institute of Standardsand Technology, Gaithersburg, MD, USA; Institute for Bioscience and BiotechnologyResearch, Rockville, MD, USA

VIBHISHA VAGHASIA • Gujarat Biotechnology Research Centre, Department of Science andTechnology, Government of Gujarat, Gandhinagar, India; Department of Botany,Bioinformatics and Climate Change, Gujarat University, Ahmedabad, India

ABHA VASHISTA • Institute of Management Studies (IMSUC), Ghaziabad, Uttar Pradesh,India

DAN WANG • State Key Laboratory of Proteomics, Beijing Proteome Research Center,National Center for Protein Sciences-Beijing (PHOENIX Center), Beijing Institute ofLifeomics, Beijing, China

BHRUGU YAGNIK • Emory Vaccine Center, Yerkes National Primate Research Center, EmoryUniversity, Atlanta, GA, USA; Department of Microbiology and Immunology, EmorySchool of Medicine, Emory University, Atlanta, GA, USA

BO YAO • Quantitative Biomedical Research Center, University of Texas SouthwesternMedical Center, Dallas, TX, USA

XIAOBO YU • State Key Laboratory of Proteomics, Beijing Proteome Research Center,National Center for Protein Sciences-Beijing (PHOENIX Center), Beijing Institute ofLifeomics, Beijing, China

CHI ZHANG • School of Biological Sciences, University of Nebraska – Lincoln, Lincoln, NE,USA

YUPENG ZHANG • State Key Laboratory of Proteomics, Beijing Proteome Research Center,National Center for Protein Sciences-Beijing (PHOENIX Center), Beijing Institute ofLifeomics, Beijing, China

DANDAN ZHENG • Department of Radiation Oncology, University of Nebraska MedicalCenter, Omaha, NE, USA

Contributors xiii