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bioRxiv Preprint

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-1b, 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 RBD domain of SARS-CoV-2. SEMA is available at and the web-interface

Shashkova, T. I.; Umerenkov, D.; Salnikov, M.; Strashnov, P. V.; Konstantinova, A. V.; Lebed, I.; Shcherbinin, D. N.; Asatryan, M. N.; Kardymon, O. L.; Ivanisenko, N. V.