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An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study

Scientific reports, 2021-02, Vol.11 (1), p.3238-21, Article 3238 [Peer Reviewed Journal]

The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2021 ;ISSN: 2045-2322 ;EISSN: 2045-2322 ;DOI: 10.1038/s41598-021-81749-9 ;PMID: 33547334

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Title:
An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study
Author: Yang, Zikun ; Bogdan, Paul ; Nazarian, Shahin
Subjects: Allergenicity ; Allergens ; Amino acid sequence ; Antigenicity ; Artificial intelligence ; Codon Usage ; Computational Biology ; Computer applications ; COVID-19 ; COVID-19 - prevention & control ; COVID-19 Vaccines - adverse effects ; COVID-19 Vaccines - chemistry ; COVID-19 Vaccines - immunology ; COVID-19 Vaccines - toxicity ; Cytotoxicity ; Deep Learning ; Design ; Drug Design ; Epitopes ; Epitopes, B-Lymphocyte - immunology ; Epitopes, T-Lymphocyte - immunology ; Humans ; Immunogenicity, Vaccine ; Infectious diseases ; Lymphocytes B ; Lymphocytes T ; Models, Molecular ; Molecular Docking Simulation ; Molecular Dynamics Simulation ; Mutation ; Neural networks ; Physicochemical properties ; Protein Conformation ; Protein structure ; Ribonucleic acid ; RNA ; RNA viruses ; RNA, Viral ; SARS-CoV-2 - chemistry ; SARS-CoV-2 - genetics ; SARS-CoV-2 - immunology ; Secondary structure ; Severe acute respiratory syndrome coronavirus 2 ; Solubility ; Spike Glycoprotein, Coronavirus - chemistry ; Spike Glycoprotein, Coronavirus - genetics ; Spike Glycoprotein, Coronavirus - immunology ; Spike protein ; T-Lymphocytes, Cytotoxic - immunology ; T-Lymphocytes, Helper-Inducer - immunology ; Vaccines ; Vaccines, Subunit - chemistry ; Vaccines, Subunit - immunology ; Viral infections
Is Part Of: Scientific reports, 2021-02, Vol.11 (1), p.3238-21, Article 3238
Description: The rampant spread of COVID-19, an infectious disease caused by SARS-CoV-2, all over the world has led to over millions of deaths, and devastated the social, financial and political entities around the world. Without an existing effective medical therapy, vaccines are urgently needed to avoid the spread of this disease. In this study, we propose an in silico deep learning approach for prediction and design of a multi-epitope vaccine (DeepVacPred). By combining the in silico immunoinformatics and deep neural network strategies, the DeepVacPred computational framework directly predicts 26 potential vaccine subunits from the available SARS-CoV-2 spike protein sequence. We further use in silico methods to investigate the linear B-cell epitopes, Cytotoxic T Lymphocytes (CTL) epitopes, Helper T Lymphocytes (HTL) epitopes in the 26 subunit candidates and identify the best 11 of them to construct a multi-epitope vaccine for SARS-CoV-2 virus. The human population coverage, antigenicity, allergenicity, toxicity, physicochemical properties and secondary structure of the designed vaccine are evaluated via state-of-the-art bioinformatic approaches, showing good quality of the designed vaccine. The 3D structure of the designed vaccine is predicted, refined and validated by in silico tools. Finally, we optimize and insert the codon sequence into a plasmid to ensure the cloning and expression efficiency. In conclusion, this proposed artificial intelligence (AI) based vaccine discovery framework accelerates the vaccine design process and constructs a 694aa multi-epitope vaccine containing 16 B-cell epitopes, 82 CTL epitopes and 89 HTL epitopes, which is promising to fight the SARS-CoV-2 viral infection and can be further evaluated in clinical studies. Moreover, we trace the RNA mutations of the SARS-CoV-2 and ensure that the designed vaccine can tackle the recent RNA mutations of the virus.
Publisher: England: Nature Publishing Group
Language: English
Identifier: ISSN: 2045-2322
EISSN: 2045-2322
DOI: 10.1038/s41598-021-81749-9
PMID: 33547334
Source: Open Access: PubMed Central
AUTh Library subscriptions: ProQuest Central
MEDLINE
Coronavirus Research Database
DOAJ Directory of Open Access Journals

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An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study

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