Improving B-cell epitope prediction and its application to global antibody–antigen docking
Open accessAbstract
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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Modelled antibody structures can be used at the Lead Identification stage to select surface exposed paratope residues for mutations or to characterize binding with respect to the cognate epitope.
“Modelled structures can be used at the Lead Identification stage to select surface exposed paratope residues for mutations [110] or to characterize the binding with respect to the cognate epitope [119].”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [119]
Arbitrary molecular surfaces appear to be indistinguishable from epitope regions, suggesting that predictions should be performed with reference to a particular antibody.
“However, arbitrary molecular surfaces appear to be indistinguishable from epitope regions [167–169] suggesting that predictions should be performed with reference to a particular antibody [116, 118–120].”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [119]
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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.
- 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
- 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
- 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
- Physical Sciences › Computer Science › Computational Theory and Mathematics › 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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