Machine learning tools used for mapping some immunogenic epitopes within the major structural proteins of the bovine coronavirus (BCoV) and for the in silico design of the multiepitope-based vaccines

Our designed multiepitope vaccine candidates per each BCoV structural protein showed high antigenicity, immunogenicity, non-allergic, non-toxic, and high-water solubility. Further studies are highly encouraged to validate the efficacy of these novel BCoV vaccines in the natural host.

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Publication Details

Journal
Frontiers in Veterinary Science
Published
2024-10-02
DOI
https://doi.org/10.3389/fvets.2024.1468890
Citations
6
Primary Topic
vaccines and immunoinformatics approaches
Type
article
Field-Weighted Citation Impact
1.40
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article

Machine learning tools used for mapping some immunogenic epitopes within the major structural proteins of the bovine coronavirus (BCoV) and for the in silico design of the multiepitope-based vaccines

Nithyadevi Duraisamy, Mohammed Cherkaoui, Abid Ullah Shah, Mohd Yasir Khan et al.
6 citations
Frontiers in Veterinary Science
vaccines and immunoinformatics approaches
1.40
article

Machine learning tools used for mapping some immunogenic epitopes within the major structural proteins of the bovine coronavirus (BCoV) and for the in silico design of the multiepitope-based vaccines

Nithyadevi Duraisamy, Mohammed Cherkaoui, Abid Ullah Shah, Mohd Yasir Khan, Reda Nacif ElAlaoui, Maged Gomaa Hemida
article en
6 citations

Abstract

Our designed multiepitope vaccine candidates per each BCoV structural protein showed high antigenicity, immunogenicity, non-allergic, non-toxic, and high-water solubility. Further studies are highly encouraged to validate the efficacy of these novel BCoV vaccines in the natural host.

Frontiers in Veterinary ScienceVol. 11
Long Island University (US)
Good health and well-being
Openalex Percentile: Top 19%
vaccines and immunoinformatics approaches
1.40
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