Abstract

BackgroundThe ability to predict antibody binding sites (aka antigenic determinants or B-cell epitopes) for a given protein is a precursor to new vaccine design and diagnostics. Among the various methods of B-cell epitope identification X-ray crystallography is one of the most reliable methods. Using these experimental data computational methods exist for B-cell epitope prediction. As the number of structures of antibody-protein complexes grows, further interest in prediction methods using 3D structure is anticipated. This work aims to establish a benchmark for 3D structure-based epitope prediction methods.ResultsTwo B-cell epitope benchmark datasets inferred from the 3D structures of antibody-protein complexes were defined. The first is a dataset of 62 representative 3D structures of protein antigens with inferred structural epitopes. The second is a dataset of 82 structures of antibody-protein complexes containing different structural epitopes. Using these datasets, eight web-servers developed for antibody and protein binding sites prediction have been evaluated. In no method did performance exceed a 40% precision and 46% recall. The values of the area under the receiver operating characteristic curve for the evaluated methods were about 0.6 for ConSurf, DiscoTope, and PPI-PRED methods and above 0.65 but not exceeding 0.70 for protein-protein docking methods when the best of the top ten models for the bound docking were considered; the remaining methods performed close to random. The benchmark datasets are included as a supplement to this paper.ConclusionIt may be possible to improve epitope prediction methods through training on datasets which include only immune epitopes and through utilizing more features characterizing epitopes, for example, the evolutionary conservation score. Notwithstanding, overall poor performance may reflect the generality of antigenicity and hence the inability to decipher B-cell epitopes as an intrinsic feature of the protein. It is an open question as to whether ultimately discriminatory features can be found.

Highlights

  • The ability to predict antibody binding sites for a given protein is a precursor to new vaccine design and diagnostics

  • To facilitate the further development of this standard, we have developed B-cell epitope benchmark datasets inferred from existing 3D structures of antibody-protein complexes

  • Structural epitope definition Three definitions of an epitope inferred from the X-ray structures of antibody-protein complexes were considered: (1) The epitope consists of protein antigen residues in which any atom of the residue looses more than 1Å2 of accessible surface area (ASA) upon antibody binding

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Summary

Introduction

The ability to predict antibody binding sites (aka antigenic determinants or B-cell epitopes) for a given protein is a precursor to new vaccine design and diagnostics. Among the various methods of B-cell epitope identification X-ray crystallography is one of the most reliable methods. Using these experimental data computational methods exist for B-cell epitope prediction. The main objective of B-cell epitope prediction is to facilitate the design of a short peptide or other molecule that can be (page number not for citation purposes). Structural methods, X-ray crystallography of antibody-antigen complexes, generally identify B-cell epitopes more reliably than functional assays [35]. The immunoinformatics software and databases developed to facilitate vaccine design have previously been reviewed [11,12]

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