{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T06:36:53Z","timestamp":1776753413373,"version":"3.51.2"},"reference-count":25,"publisher":"Oxford University Press (OUP)","issue":"16","license":[{"start":{"date-parts":[[2016,12,28]],"date-time":"2016-12-28T00:00:00Z","timestamp":1482883200000},"content-version":"vor","delay-in-days":982,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,8,15]]},"abstract":"<jats:p>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\u2013antigen docking hold the potential to facilitate the screening process by rapidly providing a list of initial poses that approximate the native complex.<\/jats:p>\n                  <jats:p>Results: We have developed a new method to identify the epitope region on the antigen, given the structures of the antibody and the antigen\u2014EpiPred. The method combines conformational matching of the antibody\u2013antigen structures and a specific antibody\u2013antigen score. We have tested the method on both a large non-redundant set of antibody\u2013antigen 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.<\/jats:p>\n                  <jats:p>Availability and implementation: Our software is available from http:\/\/www.stats.ox.ac.uk\/research\/proteins\/resources.<\/jats:p>\n                  <jats:p>Contact: \u00a0deane@stats.ox.ac.uk<\/jats:p>\n                  <jats:p>Supplementary information: \u00a0Supplementary Data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btu190","type":"journal-article","created":{"date-parts":[[2014,4,21]],"date-time":"2014-04-21T20:28:11Z","timestamp":1398112091000},"page":"2288-2294","source":"Crossref","is-referenced-by-count":172,"title":["Improving B-cell epitope prediction and its application to global antibody-antigen docking"],"prefix":"10.1093","volume":"30","author":[{"given":"Konrad","family":"Krawczyk","sequence":"first","affiliation":[{"name":"1 \u00a01Department of Statistics, Oxford University, OX1 3TG, Oxford, 2UCB Pharma, SL1 3WE Slough, UK and 3Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaofeng","family":"Liu","sequence":"additional","affiliation":[{"name":"1 \u00a01Department of Statistics, Oxford University, OX1 3TG, Oxford, 2UCB Pharma, SL1 3WE Slough, UK and 3Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Terry","family":"Baker","sequence":"additional","affiliation":[{"name":"1 \u00a01Department of Statistics, Oxford University, OX1 3TG, Oxford, 2UCB Pharma, SL1 3WE Slough, UK and 3Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiye","family":"Shi","sequence":"additional","affiliation":[{"name":"1 \u00a01Department of Statistics, Oxford University, OX1 3TG, Oxford, 2UCB Pharma, SL1 3WE Slough, UK and 3Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Charlotte M.","family":"Deane","sequence":"additional","affiliation":[{"name":"1 \u00a01Department of Statistics, Oxford University, OX1 3TG, Oxford, 2UCB Pharma, SL1 3WE Slough, UK and 3Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2014,4,21]]},"reference":[{"key":"2023012711523296000_btu190-B1","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1093\/nar\/28.1.235","article-title":"The protein data bank","volume":"28","author":"Berman","year":"2000","journal-title":"Nucleic Acids Res."},{"key":"2023012711523296000_btu190-B2","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.jmb.2012.09.021","article-title":"One target-two different binding modes: structural insights into gevokizumab and canakinumab interactions to interleukin-1\u03b2 (il-1\u03b2)","volume":"425","author":"Blech","year":"2012","journal-title":"J. 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