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DiscoTope-3.0 - Improved B-cell epitope prediction using AlphaFold2 modeling and inverse folding latent representations
Open accessPreprint — published as 10.3389/fimmu.2024.1322712 · Paper page
Abstract
Accurate computational identification of B-cell epitopes is crucial for the development of vaccines, therapies, and diagnostic tools. However, current structure-based prediction methods face limitations due to the dependency on experimentally solved structures. Here, we introduce DiscoTope-3.0, a markedly improved B-cell epitope prediction tool that innovatively employs inverse folding structure representations and a positive-unlabelled learning strategy, and is explicitly adapted for both solved and predicted structures. Our tool demonstrates a considerable improvement in performance over existing methods, accurately predicting linear and conformational epitopes across multiple independent datasets. Most notably, DiscoTope-3.0 maintains high predictive performance across solved, relaxed and predicted structures, alleviating the need for experimental validation and extending the general applicability of accurate B-cell epitope prediction by more than 3 orders of magnitude. DiscoTope-3.0 is made widely accessible on two web servers, processing over 100 structures per submission, and as a downloadable package. In addition, the servers interface with RCSB and AlphaFoldDB, facilitating large-scale prediction across over 200 million cataloged proteins. DiscoTope-3.0 is available at: https://services.healthtech.dtu.dk/service.php?DiscoTope-3.0
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Highly accurate protein structure prediction with AlphaFold
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Metadata
- Authors
- Høie, M. H., Gade, F. S., Johansen, J. M., Würtzen, C., Winther, O., Nielsen, M., & Marcatili, P.
- Affiliations
- Technical University of Denmark (DK)
- University of Copenhagen (DK)
- Corresponding author
- Nielsen, Morten
- ORCID
- Venue
- Frontiers in Immunology
- Publisher
- openRxiv
- Published
- 2023-02-05
- Volume
- 15
- Pages
- 1322712-1322712
- ISSN
- 1664-3224 (ISSN-L)
- PMC
- PMC10882062
- OpenAlex
- W4391648610
- Cited by
- 151
- References
- 1
- Citations by year
- 2026: 60 · 2025: 56 · 2024: 33 · 2023: 2
- FWCI
- 20.07
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Immunology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
- Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Novo Nordisk Fonden (NNF20OC0062606)
- Sino-Danish Center
- National Institute of Allergy and Infectious Diseases (75N93019C00001)
- 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 › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- B-cell epitope prediction, inverse folding, latent representations, positive-unlabeled learning, conformational epitopes, linear epitopes, structure-based prediction, predicted protein structures, AlphaFold, web server
- MeSH terms
- Molecular Conformation, Epitopes, B-Lymphocyte
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