Immunoinformatics and Molecular Docking Studies Predicted Potential Multiepitope-Based Peptide Vaccine and Novel Compounds against Novel SARS-CoV-2 through Virtual Screening.


Journal

BioMed research international
ISSN: 2314-6141
Titre abrégé: Biomed Res Int
Pays: United States
ID NLM: 101600173

Informations de publication

Date de publication:
2021
Historique:
received: 14 04 2020
revised: 13 08 2020
accepted: 08 02 2021
entrez: 17 3 2021
pubmed: 18 3 2021
medline: 31 3 2021
Statut: epublish

Résumé

Coronaviruses (CoVs) are enveloped positive-strand RNA viruses which have club-like spikes at the surface with a unique replication process. Coronaviruses are categorized as major pathogenic viruses causing a variety of diseases in birds and mammals including humans (lethal respiratory dysfunctions). Nowadays, a new strain of coronaviruses is identified and named as SARS-CoV-2. Multiple cases of SARS-CoV-2 attacks are being reported all over the world. SARS-CoV-2 showed high death rate; however, no specific treatment is available against SARS-CoV-2. In the current study, immunoinformatics approaches were employed to predict the antigenic epitopes against SARS-CoV-2 for the development of the coronavirus vaccine. Cytotoxic T-lymphocyte and B-cell epitopes were predicted for SARS-CoV-2 coronavirus protein. Multiple sequence alignment of three genomes (SARS-CoV, MERS-CoV, and SARS-CoV-2) was used to conserved binding domain analysis. The docking complexes of 4 CTL epitopes with antigenic sites were analyzed followed by binding affinity and binding interaction analyses of top-ranked predicted peptides with MHC-I HLA molecule. The molecular docking (Food and Drug Regulatory Authority library) was performed, and four compounds exhibiting least binding energy were identified. The designed epitopes lead to the molecular docking against MHC-I, and interactional analyses of the selected docked complexes were investigated. In conclusion, four CTL epitopes (GTDLEGNFY, TVNVLAWLY, GSVGFNIDY, and QTFSVLACY) and four FDA-scrutinized compounds exhibited potential targets as peptide vaccines and potential biomolecules against deadly SARS-CoV-2, respectively. A multiepitope vaccine was also designed from different epitopes of coronavirus proteins joined by linkers and led by an adjuvant. Our investigations predicted epitopes and the reported molecules that may have the potential to inhibit the SARS-CoV-2 virus. These findings can be a step towards the development of a peptide-based vaccine or natural compound drug target against SARS-CoV-2.

Sections du résumé

BACKGROUND BACKGROUND
Coronaviruses (CoVs) are enveloped positive-strand RNA viruses which have club-like spikes at the surface with a unique replication process. Coronaviruses are categorized as major pathogenic viruses causing a variety of diseases in birds and mammals including humans (lethal respiratory dysfunctions). Nowadays, a new strain of coronaviruses is identified and named as SARS-CoV-2. Multiple cases of SARS-CoV-2 attacks are being reported all over the world. SARS-CoV-2 showed high death rate; however, no specific treatment is available against SARS-CoV-2.
METHODS METHODS
In the current study, immunoinformatics approaches were employed to predict the antigenic epitopes against SARS-CoV-2 for the development of the coronavirus vaccine. Cytotoxic T-lymphocyte and B-cell epitopes were predicted for SARS-CoV-2 coronavirus protein. Multiple sequence alignment of three genomes (SARS-CoV, MERS-CoV, and SARS-CoV-2) was used to conserved binding domain analysis.
RESULTS RESULTS
The docking complexes of 4 CTL epitopes with antigenic sites were analyzed followed by binding affinity and binding interaction analyses of top-ranked predicted peptides with MHC-I HLA molecule. The molecular docking (Food and Drug Regulatory Authority library) was performed, and four compounds exhibiting least binding energy were identified. The designed epitopes lead to the molecular docking against MHC-I, and interactional analyses of the selected docked complexes were investigated. In conclusion, four CTL epitopes (GTDLEGNFY, TVNVLAWLY, GSVGFNIDY, and QTFSVLACY) and four FDA-scrutinized compounds exhibited potential targets as peptide vaccines and potential biomolecules against deadly SARS-CoV-2, respectively. A multiepitope vaccine was also designed from different epitopes of coronavirus proteins joined by linkers and led by an adjuvant.
CONCLUSION CONCLUSIONS
Our investigations predicted epitopes and the reported molecules that may have the potential to inhibit the SARS-CoV-2 virus. These findings can be a step towards the development of a peptide-based vaccine or natural compound drug target against SARS-CoV-2.

Identifiants

pubmed: 33728324
doi: 10.1155/2021/1596834
pmc: PMC7910514
doi:

Substances chimiques

COVID-19 Vaccines 0
Epitopes, B-Lymphocyte 0
Epitopes, T-Lymphocyte 0
Vaccines, Subunit 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1596834

Informations de copyright

Copyright © 2021 Muhammad Waqas et al.

Déclaration de conflit d'intérêts

Authors have no conflicts of interest form anyone.

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Auteurs

Muhammad Waqas (M)

Department of Bioinformatics and Biotechnology, Government College University, Faisalabad, Pakistan.

Ali Haider (A)

Department of Bioinformatics and Biotechnology, Government College University, Faisalabad, Pakistan.

Abdur Rehman (A)

Department of Bioinformatics and Biotechnology, Government College University, Faisalabad, Pakistan.

Muhammad Qasim (M)

Department of Bioinformatics and Biotechnology, Government College University, Faisalabad, Pakistan.

Ahitsham Umar (A)

Department of Bioinformatics and Biotechnology, Government College University, Faisalabad, Pakistan.

Muhammad Sufyan (M)

Department of Bioinformatics and Biotechnology, Government College University, Faisalabad, Pakistan.

Hafiza Nisha Akram (HN)

Department of Environmental Sciences, Quaid-e-Azam University, Islamabad, Pakistan.

Asif Mir (A)

Department of Biological Sciences, International Islamic University, Islamabad, Pakistan.

Roha Razzaq (R)

Department of Bioinformatics and Biotechnology, Government College University, Faisalabad, Pakistan.

Danish Rasool (D)

Department of Bioinformatics and Biotechnology, Government College University, Faisalabad, Pakistan.

Rana Adnan Tahir (RA)

Department of Biosciences, COMSATS University, Sahiwal Campus, Islamabad, Pakistan.

Sheikh Arslan Sehgal (SA)

Department of Bioinformatics and Biotechnology, Government College University, Faisalabad, Pakistan.
Department of Bioinformatics, University of Okara, Okara, Pakistan.

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