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Cited by 697vaccines and immunoinformatics approachesMachine Learning in BioinformaticsProtein Structure and Dynamics

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Reliable B Cell Epitope Predictions: Impacts of Method Development and Improved Benchmarking

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Kringelum, Jens Vindahl, Lundegaard, Claus, Lund, Ole, Nielsen, Morten·2012·PLoS Computational Biology
DOI 10.1371/journal.pcbi.1002829PMC PMC3531324
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Abstract

The interaction between antibodies and antigens is one of the most important immune system mechanisms for clearing infectious organisms from the host. Antibodies bind to antigens at sites referred to as B-cell epitopes. Identification of the exact location of B-cell epitopes is essential in several biomedical applications such as; rational vaccine design, development of disease diagnostics and immunotherapeutics. However, experimental mapping of epitopes is resource intensive making in silico methods an appealing complementary approach. To date, the reported performance of methods for in silico mapping of B-cell epitopes has been moderate. Several issues regarding the evaluation data sets may however have led to the performance values being underestimated: Rarely, all potential epitopes have been mapped on an antigen, and antibodies are generally raised against the antigen in a given biological context not against the antigen monomer. Improper dealing with these aspects leads to many artificial false positive predictions and hence to incorrect low performance values. To demonstrate the impact of proper benchmark definitions, we here present an updated version of the DiscoTope method incorporating a novel spatial neighborhood definition and half-sphere exposure as surface measure. Compared to other state-of-the-art prediction methods, Discotope-2.0 displayed improved performance both in cross-validation and in independent evaluations. Using DiscoTope-2.0, we assessed the impact on performance when using proper benchmark definitions. For 13 proteins in the training data set where sufficient biological information was available to make a proper benchmark redefinition, the average AUC performance was improved from 0.791 to 0.824. Similarly, the average AUC performance on an independent evaluation data set improved from 0.712 to 0.727. Our results thus demonstrate that given proper benchmark definitions, B-cell epitope prediction methods achieve highly significant predictive performances suggesting these tools to be a powerful asset in rational epitope discovery. The updated version of DiscoTope is available at www.cbs.dtu.dk/services/DiscoTope-2.0.

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Figure 1 Cross-validated performance. Performances of different methods for predicting B-cell epitopes evaluated on the DiscoTope dataset. From left to right: The original DiscoTope method, the uncombined log-odds ratio scores as described in text, the surface measures; UHS, RSA, FS HSE and Ta (see text) and the DiscoTope2.0 method as described in text. Performance of the original DiscoTope method was obtained from [12].
Figure 1 Cross-validated performance. Performances of different methods for predicting B-cell epitopes evaluated on the DiscoTope dataset. From left to right: The original DiscoTope method, the uncombined log-odds ratio scores as described in text, the surface measures; UHS, RSA, FS HSE and Ta (see text) and the DiscoTope2.0 method as described in text. Performance of the original DiscoTope method was obtained from [12].CC BY · PubMed Central
Figure 2 Illustration of benchmark redefinition on Lysozyme. 6 unique discontinuous epitopes have been identified for lysozyme. Including this comprehensive information on multiple epitopes for Lysozyme, the reported performance is increased. Predictions are illustrated as a heatmap on the protein surface where Red = high prediction score, Blue = low prediction score.
Figure 2 Illustration of benchmark redefinition on Lysozyme. 6 unique discontinuous epitopes have been identified for lysozyme. Including this comprehensive information on multiple epitopes for Lysozyme, the reported performance is increased. Predictions are illustrated as a heatmap on the protein surface where Red = high prediction score, Blue = low prediction score.CC BY · PubMed Central
Figure 3 Effect of benchmark redefinition and inclusion of biological units in prediction accuracy for the subset of 13 affected homology groups (see text). Refer to Table S1 for complete definition of protein names.
Figure 3 Effect of benchmark redefinition and inclusion of biological units in prediction accuracy for the subset of 13 affected homology groups (see text). Refer to Table S1 for complete definition of protein names.CC BY · PubMed Central
Figure 4 Enhance prediction accuracy by inclusion of structural data of the biological unit. Illustration of prediction for KvAP potassium channel. Left: using only one antigen chain, middle: using the biological tetramer, right: Excluding membrane and cytoplasmic residues. Predictions are illustrated as a heatmap on the protein surface where Red = high prediction score, Blue = low prediction score. Note, that the stated performances are for the PDB entry 1K4C and not the complete potassium homology group.
Figure 4 Enhance prediction accuracy by inclusion of structural data of the biological unit. Illustration of prediction for KvAP potassium channel. Left: using only one antigen chain, middle: using the biological tetramer, right: Excluding membrane and cytoplasmic residues. Predictions are illustrated as a heatmap on the protein surface where Red = high prediction score, Blue = low prediction score. Note, that the stated performances are for the PDB entry 1K4C and not the complete potassium homology group.CC BY · PubMed Central
Figure 5 Predictions for Gp120 plotted on the protein structure including bound antibody. Each residue in the structure is colored from blue to red according to its DiscoTope-2.0 score. Blue indicates low scores (predicted to be non-epitope residue) and red indicates high scores (predicted to be epitope residue). Yellow indicates possible glycosylation sites retrieved from UNIPROT accession number P04578 (www.uniprot.org). a) Gp120 surface representation and antibody cartoon representation. b) Gp120 and antibody cartoon representation. Note, the red alpha-1 helix, which is normally buried in the inner domain of Gp120 involved in Gp41∶Gp120 complex formation, is exposed in the crystal structure.
Figure 5 Predictions for Gp120 plotted on the protein structure including bound antibody. Each residue in the structure is colored from blue to red according to its DiscoTope-2.0 score. Blue indicates low scores (predicted to be non-epitope residue) and red indicates high scores (predicted to be epitope residue). Yellow indicates possible glycosylation sites retrieved from UNIPROT accession number P04578 (www.uniprot.org). a) Gp120 surface representation and antibody cartoon representation. b) Gp120 and antibody cartoon representation. Note, the red alpha-1 helix, which is normally buried in the inner domain of Gp120 involved in Gp41∶Gp120 complex formation, is exposed in the crystal structure.CC BY · PubMed Central

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Metadata

Authors
Kringelum, J. V., Lundegaard, C., Lund, O., & Nielsen, M.
Affiliations
  • Technical University of Denmark (DK)
  • National University of General San Martín (AR)
Corresponding author
Nielsen, Morten
ORCID
Venue
PLoS Computational Biology
Publisher
Public Library of Science (PLoS)
Published
2012-12-27
Volume
8
Issue
12
Pages
e1002829
ISSN
  • 1553-734X (ISSN-L)
  • 1553-7358
DOI
10.1371/journal.pcbi.1002829
PMC
PMC3531324
OpenAlex
W2100316154
Cited by
697
References
34
Citations by year
2026: 34 · 2025: 49 · 2024: 54 · 2023: 65 · 2022: 91 · 2021: 90 · 2020: 91 · 2019: 41 · 2018: 41 · 2017: 41 · 2016: 34 · 2015: 38 · 2014: 21 · 2013: 6 · 2012: 1
FWCI
8.48
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS Computational Biology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
  • Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion, other-oa)
Funders
European Commission (2007–2013, FP7 2007-2013, FP7/2007, 222773)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Keywords
B-cell epitope prediction, benchmarking, antigen-antibody reaction, surface accessibility, rational vaccine design
MeSH terms
Epitopes, B-Lymphocytes, Humans, Models, Molecular, Odds Ratio, Benchmarking

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