Towards a consensus on datasets and evaluation metrics for developing B-cell epitope prediction tools
Open accessAbstract
A B-cell epitope is the three-dimensional structure within an antigen that can be bound to the variable region of an antibody. The prediction of B-cell epitopes is highly desirable for various immunological applications, but has presented a set of unique challenges to the bioinformatics and immunology communities. Improving the accuracy of B-cell epitope prediction methods depends on a community consensus on the data and metrics utilized to develop and evaluate such tools. A workshop, sponsored by the National Institute of Allergy and Infectious Disease (NIAID), was recently held in Washington, DC to discuss the current state of the B-cell epitope prediction field. Many of the currently available tools were surveyed and a set of recommendations was devised to facilitate improvements in the currently existing tools and to expedite future tool development. An underlying theme of the recommendations put forth by the panel is increased collaboration among research groups. By developing common datasets, standardized data formats, and the means with which to consolidate information, we hope to greatly enhance the development of B-cell epitope prediction tools.
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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 [169]
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- Authors
- Greenbaum, J. A., Andersen, P. H., Blythe, M., Bui, H., Cachau, R. E., Crowe, J., Davies, M., Kolaskar, A. S., Lund, O., Morrison, S., Mumey, B., Ofran, Y., Pellequer, J., Pinilla, C., Ponomarenko, J. V., Raghava, G. P. S., van Regenmortel, M. H. V., Roggen, E. L., Sette, A., Schlessinger, A., Sollner, J., Zand, M., & Peters, B.
- Affiliations
- La Jolla Institute for Immunology (US)
- Technical University of Denmark (DK)
- Jenner Institute (GB)
- Vanderbilt University Medical Center (US)
- John Radcliffe Hospital (GB)
- Savitribai Phule Pune University (IN)
- University of California, Los Angeles (US)
- Montana State University (US)
- Tel Aviv University (IL)
- Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR)
- Torrey Pines Institute For Molecular Studies (US)
- San Diego Supercomputer Center (US)
- Institute of Microbial Technology (IN)
- Novozymes (Denmark) (DK)
- Columbia University (US)
- University of Rochester Medicine (US)
- Corresponding author
- Greenbaum, Jason A.
- ORCID
- Venue
- Journal of Molecular Recognition
- Publisher
- Wiley
- Published
- 2007-01-05
- Volume
- 20
- Issue
- 2
- Pages
- 75-82
- ISSN
- 0952-3499 (ISSN-L)
- 1099-1352
- OpenAlex
- W2137575262
- Cited by
- 244
- References
- 43
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 8 · 2023: 12 · 2022: 11 · 2021: 13 · 2020: 2 · 2019: 21 · 2018: 15 · 2017: 9 · 2016: 6 · 2015: 11 · 2014: 17 · 2013: 19 · 2012: 5
- FWCI
- 1.69
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Molecular Recognition (publishedVersion, other-oa)
- PubMed (publishedVersion)
- HAL (submittedVersion, other-oa)
- Research at the University of Copenhagen (University of Copenhagen) (submittedVersion)
- Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion)
- Funders
- National Institutes of Health
- University of California, San Francisco
- National Institute of Allergy and Infectious Diseases (NIH P41)
- National Center for Research Resources (P41-RR01081, P41RR001081)
- 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 › Chemical Synthesis and Analysis
- Keywords
- B-cell epitope prediction, benchmark dataset, evaluation metrics, antibody-antigen binding, standardized data formats, community consensus, immunoinformatics, immunoglobulin variable region
- MeSH terms
- Animals, Evaluation Studies as Topic, Health Planning Guidelines, Humans, Models, Biological, Models, Molecular, Software, Protein Structure, Secondary, Epitopes, B-Lymphocyte, Peptide Library, Sequence Analysis, Protein, Databases, Protein, Consensus
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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