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epitopes, respectively. Linear epitopes are made up of amino acidresidues that are sequential in the primary structure of the protein,

whereas conformational epitopes are formed by residues that arenot sequential but come together in the antigens tertiary struc-ture. About 90% of B-cell epitopes are conformational, meaning

they can recognize epitopes in their native state with antigen.

2 Why Epitopes and Their Mapping Are So Important

Both B- and T-cell epitopes have been used extensively in peptide- orepitope-based vaccines. Other types of vaccine design (live, killed,attenuated, and recombinant) are limited owing to safety issues inchildren and immune-compromised individuals. However, despitetheir better safety profile, peptide-based vaccines possess poor

stability, poor immunogenicity, and lack of memory response.Fortunately, with advances in technologies such as peptide synthesis,peptide modification, and the science of adjuvants, these limita-tions are no longer a bottleneck. Peptide epitopes can be modifiedby attaching one or more chains of polyethylene glycol or by incor-porating nonnatural amino acids, leading to increased stability andbioavailability (1, 2). Peptide vaccines are also advantageous inbypassing the requirement of antigen processing and delivery of aprecise and chemically defined cargo to the antigen-presentingcells (3). To address the problem of antigenic variation, multiple-

epitope vaccines can be used, targeting antigens from several strains ata time. In addition to the use of epitopes in peptide-based vaccines,epitope discovery is needed for the selective deimmunization oftherapeutic (4) and autoimmunity proteins (5).

Several experimental methods can be used for the identificationor mapping of epitopes. X-ray crystallography, nuclear magneticresonance, and electron microscopy map the structural epitopesthat are in contact with antibody, whereas methods such asPEPSCAN and enzyme-linked immunosorbent assay are func-tional in approach (6). These experimental approaches, like others,

require resources, time, and money.

3 In Silico Models for B-Cell Epitope Prediction

For decades researchers have been developing in silico models tominimize the number of experiments needed to identify or mapthe potential epitopes on the antigen surface. Because of the basicdifferences in the recognition of B- and T-cell epitopes, researchershave derived separate algorithms and tools for the two types of

epitope. This chapter discusses only B-cell epitope prediction models(linear and conformational). Although they are not very differentfrom basic B-cell epitope algorithms, T-cell epitope models havebeen reviewed in detail elsewhere (7, 8).

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In 2006 Saha and Raghava (24) used a feed-forward neuralnetwork on 700 nonredundant B-cell epitopes with an equalnumber of random peptides from UniProt and obtained ~65%accuracy. In the same year Larsen et al. (25) created an HMMmodel combined with Parker and Levitt scales to get a more accu-

rate prediction of B-cell epitopes and implemented a Web server(BepiPred). Chen et al. (26) later used SVM with an amino acidpair (AAP) antigenicity scale. They proved that unlike single aminoacid residues, few AAPs are significantly preferred in epitopes overnonepitopes and achieve a highest accuracy of 73%. El-Manzalawyet al. (27) later implemented Chens AAP scale with SVM stringkernels on homology-reduced datasets and compared this withearlier methods. They achieved an area under the curve (AUC) of0.76 and the model was implemented in the form of the Web serverBCPREDS. Recently, Wang et al. (28) designed a system called

LEPS that combines physicochemical propensity and SVMclassification and achieved a highest accuracy of 72.5% on earlierand newly created datasets.

A fixed-length input vector is a prerequisite for machine learningtechniques; therefore, most of the methods assume or fix somelength (322 amino acids) for the epitope sequences, which are infact variable (380) in length. For length fixation, truncation andextension methods originally reported by Chen et al. (26) wereused. The first in silico model that could handle variability in

epitope length was developed by El-Manzalawy et al. (29) in 2008and called FBCpred, an extension of their BCPREDS tool (27).

Another approach for allowing flexible epitope length was pub-lished by Sweredoski et al. (30) in 2009, called COBEpro.Sweredoski et al. pointed to the issue of redundant data, claimingthat earlier methods used redundant datasets and there was a bigproblem while selecting negative datasets. COBEpro is a two-stepsystem for predicting linear B-cell epitopes. It first uses SVM tomake predictions on short peptide fragments within the queryantigen sequence and then calculates an epitopic propensity score

for each residue based on the fragment predictions. COBEproachieved a cross-validated AUC up to 0.83 on the fragment epitopicpropensity scoring task and an AUC up to 0.63 on the residuepropensity scoring task. Very recently Wee et al. (31) developed aBayes feature extraction methodology coupled with SVM for theprediction of B-cell epitopes of diverse length, which they termedBayesB. Table 1provides an updated list of tools for linear B-cellepitope prediction.

Unlike linear B-cell epitopes, conformational epitope prediction

models were limited by the need to understand antigenantibody(AgAb) complex structures before applying these algorithms.

As with the linear epitopes, researchers started by seeking structural

3.1.2 In Silico Models

for the Variable-Length

B-Cell Epitopes

3.2 In Silico Models

for ConformationalB-Cell Epitopes

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135In Silico Models for B-Cell Epitopes

the antigen epitopes. After binding to the corresponding antibody,these mimotopes are mapped onto the antigen structure. Severalalgorithms that have been developed to assist in this process areshown in Table 3.

4 Mathematical Models for B-Cell Receptor Signaling

The signals mediated through the B-cell antigen receptor (BCR)are critical to B-cell development and response to antigens.Defective BCR signaling leads to impaired B-cell development,immunodeficiency, and autoimmunity (43, 44). Several experi-mental techniques including microscopy and live cell imaging areused; limitations include the dynamic and interactive nature of bio-logical systems. Understanding these networks requires the devel-opment of mathematical and computational models, which broadly

come under the umbrella of systems biology (45, 46). One of thecore tasks in system biology is the reconstruction of the regulatory,interacting, and signaling networks in the cell after perturbation.

Table 3

Epitope mapping using phage display peptides

Tool Description Web site Reference

LocaPep Selection of seeds and clusterssearching

http://atenea.montes.upm.es

Pacios et al. (54)

MimoPro Dynamic programming, branch andbound and compactness factor

http://informatics.nenu.edu.cn/MimoPro

Chen et al. (55)

Pep3DSearch Implementation of ant colonyoptimization algorithm

http://kyc.nenu.edu.cn/Pep3DSearch/

Huang et al. (56)

PEPITOPE Implementation of PepSurf andMapitope

http://pepitope.tau.ac.il/ Mayrose et al. (57)

PepSurf Stochastic-based color-coding method http://pepitope.tau.ac.il/ Mayrose et al. (58)

MEPS Surface ensemble and Cdistances http://www.caspur.it/meps

Castrignanet al. (59)

Mapitope Physicochemical properties ofmimotopes

http://pepitope.tau.ac.il/ Bublil et al. (60)

MIMOP MimAlign and MimCons Software available uponrequest

Moreau et al. (61)

MIMOX Mimotope alignments and residueclustering

http://web.kuicr.kyoto-u.ac.jp/~hjian/mimox

Huang et al. (62)

3DEX Physicochemical neighborhood of

Cor Catoms

Windows-based software Schreiber et al. (63)

http://atenea.montes.upm.es/http://atenea.montes.upm.es/http://informatics.nenu.edu.cn/MimoProhttp://informatics.nenu.edu.cn/MimoProhttp://kyc.nenu.edu.cn/Pep3DSearch/http://kyc.nenu.edu.cn/Pep3DSearch/http://pepitope.tau.ac.il/http://pepitope.tau.ac.il/http://www.caspur.it/mepshttp://www.caspur.it/mepshttp://pepitope.tau.ac.il/http://web.kuicr.kyoto-u.ac.jp/~hjian/mimoxhttp://web.kuicr.kyoto-u.ac.jp/~hjian/mimoxhttp://web.kuicr.kyoto-u.ac.jp/~hjian/mimoxhttp://web.kuicr.kyoto-u.ac.jp/~hjian/mimoxhttp://pepitope.tau.ac.il/http://www.caspur.it/mepshttp://www.caspur.it/mepshttp://pepitope.tau.ac.il/http://pepitope.tau.ac.il/http://kyc.nenu.edu.cn/Pep3DSearch/http://kyc.nenu.edu.cn/Pep3DSearch/http://informatics.nenu.edu.cn/MimoProhttp://informatics.nenu.edu.cn/MimoProhttp://atenea.montes.upm.es/http://atenea.montes.upm.es/

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A major step toward system biology was taken by the Alliancefor Cellular Signaling (AfCS, http://www.signaling-gateway.org/),

whose objective is to delineate the signaling pathways for B cells andmurine macrophages after stimulation with various ligands (47).The AfCS measures cytokine secretion, protein localization, and

proteinprotein interaction, in addition to cellular calcium, cyclicadenosine monophosphate, and gene expression levels. Once thesehigh-throughput experiments are completed, data are deposited inthe University of California, San Diego, Signaling Gateway reposi-tory. Additionally AfCS projects attempt to understand the BCRclustering after antigen cross-linking using Monte Carlo models(48). Network analysis tools include CellNetAnalyzer (49),SQUAD (50), SEBINI, CABIN (51), and others as reviewed bySuresh Babu et al. (45), which will help us better understand thecomplex signaling networks present in the immune cells.

Acknowledgments

H.R.A. is financially supported by the Council of Scientific andIndustrial Research (CSIR), New Delhi, India.

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