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Immunoinformatics‐aided identification of T cell and B cell epitopes in the surface glycoprotein of 2019‐nCoV

Open access
Baruah, Vargab, Bose, Sujoy·2020·Journal of Medical Virology

Abstract

The 2019 novel coronavirus (2019‐nCoV) outbreak has caused a large number of deaths with thousands of confirmed cases worldwide, especially in East Asia. This study took an immunoinformatics approach to identify significant cytotoxic T lymphocyte (CTL) and B cell epitopes in the 2019‐nCoV surface glycoprotein. Also, interactions between identified CTL epitopes and their corresponding major histocompatibility complex (MHC) class I supertype representatives prevalent in China were studied by molecular dynamics simulations. We identified five CTL epitopes, three sequential B cell epitopes and five discontinuous B cell epitopes in the viral surface glycoprotein. Also, during simulations, the CTL epitopes were observed to be binding MHC class I peptide‐binding grooves via multiple contacts, with continuous hydrogen bonds and salt bridge anchors, indicating their potential in generating immune responses. Some of these identified epitopes can be potential candidates for the development of 2019‐nCoV vaccines.

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Metadata

Authors
Baruah, V., & Bose, S.
Venue
Journal of Medical Virology
Publisher
Wiley
Published
2020-03-03
Volume
92
Issue
5
Pages
495-500
ISSN
  • 0146-6615 (ISSN-L)
  • 1096-9071
OpenAlex
W3005011674
Cited by
268
References
2
FWCI
17.15
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Immunoinformatics‐aided identification of T cell and B cell epitopes in the surface glycoprotein of 2019‐nCoV. https://app.cochat.ai/papers/10.1002/jmv.25698
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Baruah, V., & Bose, S. (2020). Immunoinformatics‐aided identification of T cell and B cell epitopes in the surface glycoprotein of 2019‐nCoV. Journal of Medical Virology. https://doi.org/10.1002/jmv.25698

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.