Journal
BIOINFORMATICS
Volume 30, Issue 16, Pages 2288-2294Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btu190
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Funding
- UCB Pharma
- Engineering and Physical Sciences Research Council (EPSRC)
- EPSRC [EP/K032402/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/K032402/1] Funding Source: researchfish
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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.
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