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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

Open access
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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Fig. 1. Left: Example of a case when intramolecular distances can provide information about which intermolecular contacts can exist. The antibody–antigen contacts between Tyr-22 and Lys-27 and Gly-56 and Lys-34 (blue dashes) can exist as the intramolecular distance between Tyr-22 and Gly-56 is 9.4 Å and the distance between the two Lys residues is 10.2 Å. The difference between those two intramolecular distances is 0.8 Å, which is below the cutoff of 1 Å. As a counterexample, the contacts between Tyr-22 and Lys-27 and Asp-102 and Lys-34 (black dashes) cannot be satisfied simultaneously because the intramolecular distance between Tyr-22 and Asp-102 is 17.5 Å. Right: The top epitope prediction for the antigen 1boy (human tissue factor, the unbound form of the antigen complexed in 1ahw in H-test). The prediction consists of a set of residues, which are considered to constitute the general area of the epitope. The true positives are shown in green, false positives in teal, false negatives in red and true negatives in dark blue. This prediction achieved 36% precision and 94% recall. (The target comes from the dataset H-test, and thus, the antibody used in the prediction was a homology model and the corresponding antigen was in the unbound form)
Fig. 1. Left: Example of a case when intramolecular distances can provide information about which intermolecular contacts can exist. The antibody–antigen contacts between Tyr-22 and Lys-27 and Gly-56 and Lys-34 (blue dashes) can exist as the intramolecular distance between Tyr-22 and Gly-56 is 9.4 Å and the distance between the two Lys residues is 10.2 Å. The difference between those two intramolecular distances is 0.8 Å, which is below the cutoff of 1 Å. As a counterexample, the contacts between Tyr-22 and Lys-27 and Asp-102 and Lys-34 (black dashes) cannot be satisfied simultaneously because the intramolecular distance between Tyr-22 and Asp-102 is 17.5 Å. Right: The top epitope prediction for the antigen 1boy (human tissue factor, the unbound form of the antigen complexed in 1ahw in H-test). The prediction consists of a set of residues, which are considered to constitute the general area of the epitope. The true positives are shown in green, false positives in teal, false negatives in red and true negatives in dark blue. This prediction achieved 36% precision and 94% recall. (The target comes from the dataset H-test, and thus, the antibody used in the prediction was a homology model and the corresponding antigen was in the unbound form)CC BY · PubMed Central
Fig. 2. Success rates of rescoring compared with the raw decoy lists given by the docking algorithms. We show results for each docking program (ZDOCK or ClusPro) on each test set (X-test or H-test) for top one, five and ten results. The leftmost bars are the number of times our global docking pipeline improved results. Bars that are second to left are the corresponding number of cases when including epitope information made the results worse. Bars that are second to right are the number of times including the epitope information did not change the raw result. The rightmost bars are the number of cases for which both procedures reported no close-to-native decoys. See Supplementary Section 6 for the per-complex information. (A) Success rate of ClusPro on dataset X-test. (B) Success rate of ZDOCK on dataset X-test. (C) Success rate of ClusPro on dataset H-test. (D) Success rate of ZDOCK on dataset H-test
Fig. 2. Success rates of rescoring compared with the raw decoy lists given by the docking algorithms. We show results for each docking program (ZDOCK or ClusPro) on each test set (X-test or H-test) for top one, five and ten results. The leftmost bars are the number of times our global docking pipeline improved results. Bars that are second to left are the corresponding number of cases when including epitope information made the results worse. Bars that are second to right are the number of times including the epitope information did not change the raw result. The rightmost bars are the number of cases for which both procedures reported no close-to-native decoys. See Supplementary Section 6 for the per-complex information. (A) Success rate of ClusPro on dataset X-test. (B) Success rate of ZDOCK on dataset X-test. (C) Success rate of ClusPro on dataset H-test. (D) Success rate of ZDOCK on dataset H-testCC BY · PubMed Central

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Metadata

Authors
Krawczyk, K., Liu, X., Baker, T., Shi, J., & Deane, C. M.
Affiliations
Chinese Academy of Sciences (CN)
Corresponding author
Deane, Charlotte M.
ORCID
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
Licence
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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