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...
| Main Authors: | , , , , |
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| Format: | Journal article |
| Language: | English |
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Oxford University Press
2014
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| _version_ | 1826258026307256320 |
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| author | Krawczyk, K Liu, X Baker, T Shi, J Deane, C |
| author_facet | Krawczyk, K Liu, X Baker, T Shi, J Deane, C |
| author_sort | Krawczyk, K |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-06T18:27:30Z |
| format | Journal article |
| id | oxford-uuid:087d0593-58a5-49d4-94d5-c06a322aeda2 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T18:27:30Z |
| publishDate | 2014 |
| publisher | Oxford University Press |
| record_format | dspace |
| spelling | oxford-uuid:087d0593-58a5-49d4-94d5-c06a322aeda22022-03-26T09:13:11ZImproving B-cell epitope prediction and its application to global antibody-antigen docking.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:087d0593-58a5-49d4-94d5-c06a322aeda2EnglishSymplectic Elements at OxfordOxford University Press2014Krawczyk, KLiu, XBaker, TShi, JDeane, CMOTIVATION: 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. |
| spellingShingle | Krawczyk, K Liu, X Baker, T Shi, J Deane, C Improving B-cell epitope prediction and its application to global antibody-antigen docking. |
| title | Improving B-cell epitope prediction and its application to global antibody-antigen docking. |
| title_full | Improving B-cell epitope prediction and its application to global antibody-antigen docking. |
| title_fullStr | Improving B-cell epitope prediction and its application to global antibody-antigen docking. |
| title_full_unstemmed | Improving B-cell epitope prediction and its application to global antibody-antigen docking. |
| title_short | Improving B-cell epitope prediction and its application to global antibody-antigen docking. |
| title_sort | improving b cell epitope prediction and its application to global antibody antigen docking |
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