Abstract

Accurate prediction of B-cell antigenic epitopes is important for immunologic research and medical applications, but compared with other bioinformatic problems, antigenic epitope prediction is more challenging because of the extreme variability of antigenic epitopes, where the paratope on the antibody binds specifically to a given epitope with high precision. In spite of the continuing efforts in the past decade, the problem remains unsolved and therefore still attracts a lot of attention from bioinformaticists. Recently, several discontinuous epitope prediction servers became available, and it is intriguing to review all existing methods and evaluate their performances on the same benchmark. In addition, these methods are also compared against common binding site prediction algorithms, since they have been frequently used as substitutes in the absence of good epitope prediction methods.

Highlights

  • Antigenic epitopes are regions of the antigen protein surface that are preferentially recognized by antibodies

  • Cell antigenic epitopes is of direct help to the design of vaccine components and immuno-diagnostic reagents

  • Due to computational complexity and the limited number of known antibodyantigen complex structures, only a limited number of prediction methods exist for discontinuous epitope prediction: CEP [14], DiscoTope [15], BEpro(PEPITO) [16], ElliPro [17], SEPPA

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Summary

Introduction

Antigenic epitopes are regions of the antigen protein surface that are preferentially recognized by antibodies. B-cell antigenic epitopes are classified as either continuous or discontinuous. The majority of available epitope prediction methods focus on continuous epitopes [1,2,3,4,5,6,7,8,9,10,11,12]. Discontinuous epitopes dominate most antigenic epitope families [13]. Since currently all discontinuous epitope prediction methods require the three-dimensional (3D) structures of antigenic proteins, the small number of available antigen-antibody complex structures greatly limits the development of reliable discontinuous epitope prediction methods. An unbiased benchmark set is very much in demand [21,24]

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