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BepiPred-3.0: Improved B-cell epitope prediction using protein language models
Open accessPreprint — published as 10.1002/pro.4497 · Paper page
Abstract
B-cell epitope prediction tools are of great medical and commercial interest due to their practical applications in vaccine development. The introduction of protein language models (LM) trained on unprecedented large datasets of protein sequences and structures, tap into a powerful numeric representation that can be exploited to accurately predict local and global protein structural features from amino acid sequences only. In this paper, we present BepiPred 3.0, a sequence-based epitope prediction tool that, by exploiting LM embeddings, greatly improves the prediction accuracy for both linear and conformational epitope prediction on several independent test sets. Furthermore, by carefully selecting additional input variables and epitope residue annotation strategy, performance can be further improved, thus achieving extraordinary results. Our tool can predict epitopes across hundreds of sequences in mere minutes. It is freely available as a web server with a user-friendly interface to navigate the results, as well as a standalone downloadable package.
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Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
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Research works printed but not linked: 24
- 10.1110/ps.062405906 · DOI
- 10.1093/nar/gkaa1100 · DOI · Paper page
- 10.1093/nar/28.1.235 · DOI · Paper page
- 2022, Proteinbert: a universal deep-learning model of protein sequence and function, Bioinformatics, 38, 2102, 10.1093/bioinformatics/btac020 · DOI · Paper page
- 2022, Epitope3d: A machine learning method for conformational b-cell epitope prediction, Briefings in Bioinformatics, 23, bbab423, 10.1093/bib/bbab423 · DOI
- Høie, M. H. , Kiehl, E. N. , Petersen, B. , Nielsen, M. , Winther, O. , Nielsen, H. , Hallgren, J. , and Marcatili, P. (2022). NetSurfP-3.0: accurate and fast prediction of protein structural features by protein language models and deep learning. Nucleic Acids Research. gkac439. · DOI
- 10.1093/nar/gkx346 · DOI
- 10.1093/nar/gkv535 · DOI
- 2019, Netsurfp-2.0: Improved prediction of protein structural features by integrated deep learning, Proteins: Structure, Function, and Bioinformatics, 87, 520, 10.1002/prot.25674 · DOI
- 10.1016/j.molimm.2012.06.001 · DOI · Paper page
- 10.1093/nar/gki460 · DOI
- 10.1186/1471-2105-9-514 · DOI
- 10.1073/pnas.2016239118 · DOI · Paper page
- 2006, Prediction of continuous b-cell epitopes in an antigen using recurrent neural network, Proteins: Structure, Function and Genetics, 65, 40, 10.1002/prot.21078 · DOI
- 2017, Fundamentals and methods for t- and b-cell epitope prediction, Journal of Immunology Research, 2017, 2680160
- 2014, Antibody informatics for drug discovery, B B a - Proteins and Proteomics, 1844, 2002, 10.1016/j.bbapap.2014.07.006 · DOI
- 10.1371/journal.pone.0062216 · DOI
- 10.1038/nbt.3988 · DOI · Paper page
- 2020, Fast and flexible protein design using deep graph neural networks, Cell Systems, 11, 402, 10.1016/j.cels.2020.08.016 · DOI
- 10.1093/nar/gkp417 · DOI
- Sweredoski, M. J. and Baldi, P. (2008). Pepito: improved discontinuous b-cell epitope prediction using multiple distance thresholds and half sphere exposure. · DOI
- 10.1093/nar/gky1006 · DOI
- 10.1371/journal.pone.0045152 · DOI
- 10.1093/nar/gkz413 · DOI
Metadata
- Authors
- Clifford, J., Høie, M. H., Nielsen, M., Deleuran, S., Peters, B., & Marcatili, P.
- Affiliations
- Technical University of Denmark (DK)
- La Jolla Institute for Immunology (US)
- Corresponding author
- Clifford, Joakim
- ORCID
- Venue
- Protein Science
- Publisher
- openRxiv
- Published
- 2022-07-12
- Volume
- 31
- Issue
- 12
- Pages
- e4497-e4497
- ISSN
- 0961-8368 (ISSN-L)
- PMC
- PMC9679979
- OpenAlex
- W4308773001
- Cited by
- 296
- References
- 1
- Citations by year
- 2026: 96 · 2025: 95 · 2024: 68 · 2023: 34 · 2022: 3
- FWCI
- 16.74
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- Protein Science (publishedVersion, cc-by-nc)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion, cc-by-nc)
- 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, protein language models, linear epitopes, conformational epitopes, sequence-based prediction, vaccine development, web server
- MeSH terms
- Amino Acid Sequence, Language, Epitope Mapping, Epitopes, B-Lymphocyte, Computational Biology
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