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 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 structures and extending the general applicability of accurate B-cell epitope prediction by 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.
Originalsprog | Engelsk |
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Artikelnummer | 1322712 |
Tidsskrift | Frontiers in Immunology |
Vol/bind | 15 |
Antal sider | 12 |
ISSN | 1664-3224 |
DOI | |
Status | Udgivet - 2024 |
Bibliografisk note
Funding Information:The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was in part funded by National Institute of Allergy and Infectious Diseases (NIAID), under award number 75N93019C00001. MH acknowledges the Sino-Danish Center [2021]. Funding for open access charge: Internal Funding from the University.
Funding Information:
The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was in part funded by National Institute of Allergy and Infectious Diseases (NIAID), under award number 75N93019C00001. MH acknowledges the Sino-Danish Center [2021]. Funding for open access charge: Internal Funding from the University.
Publisher Copyright:
Copyright © 2024 Høie, Gade, Johansen, Würtzen, Winther, Nielsen and Marcatili.