@ARTICLE{10.3389/fimmu.2022.960985, AUTHOR={Shashkova, Tatiana I. and Umerenkov, Dmitriy and Salnikov, Mikhail and Strashnov, Pavel V. and Konstantinova, Alina V. and Lebed, Ivan and Shcherbinin, Dmitriy N. and Asatryan, Marina N. and Kardymon, Olga L. and Ivanisenko, Nikita V.}, TITLE={SEMA: Antigen B-cell conformational epitope prediction using deep transfer learning}, JOURNAL={Frontiers in Immunology}, VOLUME={13}, YEAR={2022}, URL={https://www.frontiersin.org/articles/10.3389/fimmu.2022.960985}, DOI={10.3389/fimmu.2022.960985}, ISSN={1664-3224}, ABSTRACT={One of the primary tasks in vaccine design and development of immunotherapeutic drugs is to predict conformational B-cell epitopes corresponding to primary antibody binding sites within the antigen tertiary structure. To date, multiple approaches have been developed to address this issue. However, for a wide range of antigens their accuracy is limited. In this paper, we applied the transfer learning approach using pretrained deep learning models to develop a model that predicts conformational B-cell epitopes based on the primary antigen sequence and tertiary structure. A pretrained protein language model, ESM-1v, and an inverse folding model, ESM-IF1, were fine-tuned to quantitatively predict antibody-antigen interaction features and distinguish between epitope and non-epitope residues. The resulting model called SEMA demonstrated the best performance on an independent test set with ROC AUC of 0.76 compared to peer-reviewed tools. We show that SEMA can quantitatively rank the immunodominant regions within the SARS-CoV-2 RBD domain. SEMA is available at https://github.com/AIRI-Institute/SEMAi and the web-interface http://sema.airi.net.} }