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Immunoinformatics approach of epitope prediction for SARS-CoV-2

BACKGROUND: The novel coronavirus (SARS-CoV-2) caused lethal infections worldwide during an unprecedented pandemic. Identification of the candidate viral epitopes is the first step in the design of vaccines against the viral infection. Several immunoinformatic approaches were employed to identify th...

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Autores principales: Awad, Nourelislam, Mohamed, Rania Hassan, Ghoneim, Nehal I., Elmehrath, Ahmed O., El-Badri, Nagwa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9019534/
https://www.ncbi.nlm.nih.gov/pubmed/35441904
http://dx.doi.org/10.1186/s43141-022-00344-1
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author Awad, Nourelislam
Mohamed, Rania Hassan
Ghoneim, Nehal I.
Elmehrath, Ahmed O.
El-Badri, Nagwa
author_facet Awad, Nourelislam
Mohamed, Rania Hassan
Ghoneim, Nehal I.
Elmehrath, Ahmed O.
El-Badri, Nagwa
author_sort Awad, Nourelislam
collection PubMed
description BACKGROUND: The novel coronavirus (SARS-CoV-2) caused lethal infections worldwide during an unprecedented pandemic. Identification of the candidate viral epitopes is the first step in the design of vaccines against the viral infection. Several immunoinformatic approaches were employed to identify the SARS-CoV-2 epitopes that bind specifically with the major histocompatibility molecules class I (MHC-I). We utilized immunoinformatic tools to analyze the whole viral protein sequences, to identify the SARS-CoV-2 epitopes responsible for binding to the most frequent human leukocyte antigen (HLA) alleles in the Egyptian population. These alleles were also found with high frequency in other populations worldwide. RESULTS: Molecular docking approach showed that using the co-crystallized MHC-I and T cell receptor (TCR) instead of using MHC-I structure only, significantly enhanced docking scores and stabilized the conformation, as well as the binding affinity of the identified SARS-CoV-2 epitopes. Our approach directly predicts 7 potential vaccine subunits from the available SARS-CoV-2 spike and ORF1ab protein sequence. This prediction has been confirmed by published experimentally validated and in silico predicted spike epitope. On the other hand, we predicted novel epitopes (RDLPQGFSA and FCLEASFNY) showing high docking scores and antigenicity response with both MHC-I and TCR. Moreover, antigenicity, allergenicity, toxicity, and physicochemical properties of the predicted SARS-CoV-2 epitopes were evaluated via state-of-the-art bioinformatic approaches, showing high efficacy of the proposed epitopes as a vaccine candidate. CONCLUSION: Our predicted SARS-CoV-2 epitopes can facilitate vaccine development to enhance the immunogenicity against SARS-CoV-2 and provide supportive data for further experimental validation. Our proposed molecular docking approach of exploiting both MHC and TCR structures can be used to identify potential epitopes for most microbial pathogens, provided the crystal structure of MHC co-crystallized with TCR. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s43141-022-00344-1.
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spelling pubmed-90195342022-04-20 Immunoinformatics approach of epitope prediction for SARS-CoV-2 Awad, Nourelislam Mohamed, Rania Hassan Ghoneim, Nehal I. Elmehrath, Ahmed O. El-Badri, Nagwa J Genet Eng Biotechnol Research BACKGROUND: The novel coronavirus (SARS-CoV-2) caused lethal infections worldwide during an unprecedented pandemic. Identification of the candidate viral epitopes is the first step in the design of vaccines against the viral infection. Several immunoinformatic approaches were employed to identify the SARS-CoV-2 epitopes that bind specifically with the major histocompatibility molecules class I (MHC-I). We utilized immunoinformatic tools to analyze the whole viral protein sequences, to identify the SARS-CoV-2 epitopes responsible for binding to the most frequent human leukocyte antigen (HLA) alleles in the Egyptian population. These alleles were also found with high frequency in other populations worldwide. RESULTS: Molecular docking approach showed that using the co-crystallized MHC-I and T cell receptor (TCR) instead of using MHC-I structure only, significantly enhanced docking scores and stabilized the conformation, as well as the binding affinity of the identified SARS-CoV-2 epitopes. Our approach directly predicts 7 potential vaccine subunits from the available SARS-CoV-2 spike and ORF1ab protein sequence. This prediction has been confirmed by published experimentally validated and in silico predicted spike epitope. On the other hand, we predicted novel epitopes (RDLPQGFSA and FCLEASFNY) showing high docking scores and antigenicity response with both MHC-I and TCR. Moreover, antigenicity, allergenicity, toxicity, and physicochemical properties of the predicted SARS-CoV-2 epitopes were evaluated via state-of-the-art bioinformatic approaches, showing high efficacy of the proposed epitopes as a vaccine candidate. CONCLUSION: Our predicted SARS-CoV-2 epitopes can facilitate vaccine development to enhance the immunogenicity against SARS-CoV-2 and provide supportive data for further experimental validation. Our proposed molecular docking approach of exploiting both MHC and TCR structures can be used to identify potential epitopes for most microbial pathogens, provided the crystal structure of MHC co-crystallized with TCR. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s43141-022-00344-1. Springer Berlin Heidelberg 2022-04-20 /pmc/articles/PMC9019534/ /pubmed/35441904 http://dx.doi.org/10.1186/s43141-022-00344-1 Text en © The Author(s) 2022, corrected publication 2022 https://creativecommons.org/licenses/by/4.0/This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research
Awad, Nourelislam
Mohamed, Rania Hassan
Ghoneim, Nehal I.
Elmehrath, Ahmed O.
El-Badri, Nagwa
Immunoinformatics approach of epitope prediction for SARS-CoV-2
title Immunoinformatics approach of epitope prediction for SARS-CoV-2
title_full Immunoinformatics approach of epitope prediction for SARS-CoV-2
title_fullStr Immunoinformatics approach of epitope prediction for SARS-CoV-2
title_full_unstemmed Immunoinformatics approach of epitope prediction for SARS-CoV-2
title_short Immunoinformatics approach of epitope prediction for SARS-CoV-2
title_sort immunoinformatics approach of epitope prediction for sars-cov-2
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9019534/
https://www.ncbi.nlm.nih.gov/pubmed/35441904
http://dx.doi.org/10.1186/s43141-022-00344-1
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