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Updated: May 1, 2026
A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes
Published on: March 25, 2014
Improving B-cell epitope prediction and its application to global antibody-antigen docking
Konrad Krawczyk1, Xiaofeng Liu1, Terry Baker1
1Department of Statistics, Oxford University, OX1 3TG, Oxford, UCB Pharma, SL1 3WE Slough, UK and Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China.
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 from http://www.stats.ox.ac.uk/research/proteins/resources.
