Structural bioinformatics Advance Access publication April 21, 2014 Improving B-cell epitope prediction and its application to global antibody-antigen docking

Motivation: Antibodies are currently the most important class of biopharmaceuticals. Development of such antibody-based drugs depends on costly and time-consuming screening campaigns. Computational techniques such as antibody–antigen docking hold the potential to facilitate the screening process by rapidly providing a list of initial poses that approximate the native complex. Results: We have developed a new method to identify the epitope region on the antigen, given the structures of the antibody and the antigen—EpiPred. The method combines conformational matching of the antibody–antigen structures and a specific antibody–antigen score. We have tested the method on both a large non-redundant set of antibody–antigen complexes and on homology models of the antibodies and/or the unbound antigen structure. On a non-redundant test set, our epitope prediction method achieves 44 % recall at 14% precision against 23 % recall at 14 % precision for a background random distribution. We use our epitope predictions to rescore the global docking results of two rigid-body docking algorithms: ZDOCK and ClusPro. In both cases including our epitope, prediction increases the number of near-native poses found among the top decoys. Availability and implementation: Our software is available fro