SEMA: Antigen B-cell conformational epitope prediction using deep transfer learning.
Front Immunol
; 13: 960985, 2022.
Article
in English
| MEDLINE | ID: covidwho-2154722
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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Vaccines
/
COVID-19
Type of study:
Prognostic study
Topics:
Vaccines
Limits:
Humans
Language:
English
Journal:
Front Immunol
Year:
2022
Document Type:
Article
Affiliation country:
Fimmu.2022.960985
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