Cited by 174vaccines and immunoinformatics approachesProtein Structure and DynamicsComputational Drug Discovery Methods

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Improving B-cell epitope prediction and its application to global antibody–antigen docking

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Krawczyk, Konrad, Liu, Xiaofeng, Baker, Terry, Shi, Jiye, Deane, Charlotte M.·2014·Bioinformatics

DOI 10.1093/bioinformatics/btu190PMC PMC4207425

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Abstract

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.

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Authors: Krawczyk, K., Liu, X., Baker, T., Shi, J., & Deane, C. M.
Affiliations: Chinese Academy of Sciences (CN)
Corresponding author: Deane, Charlotte M.
ORCID: Krawczyk, Konrad
Liu, Xiaofeng
Baker, Terry
Shi, Jiye
Deane, Charlotte M.
Venue: Bioinformatics
Publisher: Oxford University Press (OUP)
Published: 2014-04-21
Volume: 30
Issue: 16
Pages: 2288-2294
ISSN: 1367-4803 (ISSN-L)
1367-4811
DOI: 10.1093/bioinformatics/btu190
PMC: PMC4207425
OpenAlex: W2163174206
Cited by: 174
References: 27
Citations by year: 2026: 8 · 2025: 17 · 2024: 22 · 2023: 19 · 2022: 28 · 2021: 19 · 2020: 17 · 2019: 10 · 2018: 14 · 2017: 6 · 2016: 6 · 2015: 7 · 2014: 1
FWCI: 2.72
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
creativecommons.org/licenses/by/3.0/ (vor, 982 d)
Locations: Bioinformatics (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Funders: Engineering and Physical Sciences Research Council (EP/K032402/1)
UCB Pharma
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Keywords: B-cell epitope prediction, antibody-antigen docking, immune complexes, homology model
MeSH terms: Algorithms, Antigen-Antibody Complex, Humans, Software, Epitopes, B-Lymphocyte, Molecular Docking Simulation

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Improving B-cell epitope prediction and its application to global antibody–antigen docking

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