title: Improving B-cell epitope prediction and its application to global antibody-antigen docking.
abstract: BACKGROUND
  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.
  
  
  METHODS
  Our software is available from http://www.stats.ox.ac.uk/research/proteins/resources.
authors.affiliation: Department 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.
authors.forename: Konrad
authors.initials: K
authors.lastname: Krawczyk
authors.affiliation: Department 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.
authors.forename: Xiaofeng
authors.initials: X
authors.lastname: Liu
authors.affiliation: Department 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.
authors.forename: Terry
authors.initials: T
authors.lastname: Baker
authors.affiliation: Department 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, ChinaDepartment 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.
authors.forename: Jiye
authors.initials: J
authors.lastname: Shi
authors.affiliation: Department 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.
authors.forename: Charlotte M
authors.initials: CM
authors.lastname: Deane
mesh_terms: D000465:Algorithms; D000936:Antigen-Antibody Complex / Q000737:chemistry*; D018985:Epitopes, B-Lymphocyte / Q000737:chemistry*; D006801:Humans; D062105:Molecular Docking Simulation / Q000379:methods*; D012984:Software
chemical_list: D000936:Antigen-Antibody Complex; D018985:Epitopes, B-Lymphocyte
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