@ARTICLE{discotope3, AUTHOR={Høie, Magnus Haraldson and Gade, Frederik Steensgaard and Johansen, Julie Maria and Würtzen, Charlotte and Winther, Ole and Nielsen, Morten and Marcatili, Paolo }, TITLE={DiscoTope-3.0: improved B-cell epitope prediction using inverse folding latent representations}, JOURNAL={Frontiers in Immunology}, VOLUME={15}, YEAR={2024}, URL={https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2024.1322712}, DOI={10.3389/fimmu.2024.1322712}, ISSN={1664-3224}, 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. DiscoTope-3.0 is available at: https://services.healthtech.dtu.dk/service.php?DiscoTope-3.0.

} }