citation.bib

@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={<p>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: <ext-link ext-link-type="uri" xlink:href="https://services.healthtech.dtu.dk/service.php?DiscoTope-3.0" xmlns:xlink="http:https://www.w3.org/1999/xlink">https://services.healthtech.dtu.dk/service.php?DiscoTope-3.0</ext-link>.</p>}
}