1. SEMA: Antigen B-cell conformational epitope prediction using deep transfer learning.
- Author
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Shashkova, Tatiana I., Umerenkov, Dmitriy, Salnikov, Mikhail, Strashnov, Pavel V., Konstantinova, Alina V., Lebed, Ivan, Shcherbinin, Dmitriy N., Asatryan, Marina N., Kardymon, Olga L., and Ivanisenko, Nikita V.
- Subjects
DEEP learning ,ANTIGENS ,BINDING sites ,TERTIARY structure ,PROTEIN models ,CONFORMATIONAL analysis - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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