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

Abstract 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 avo...

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Autores principales:	Zikun Yang, Paul Bogdan, Shahin Nazarian
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/11a53413f913468ab9b7cb55f95e4071
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spelling	oai:doaj.org-article:11a53413f913468ab9b7cb55f95e40712021-12-02T10:44:08ZAn in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study10.1038/s41598-021-81749-92045-2322https://doaj.org/article/11a53413f913468ab9b7cb55f95e40712021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81749-9https://doaj.org/toc/2045-2322Abstract 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.Zikun YangPaul BogdanShahin NazarianNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-21 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zikun Yang Paul Bogdan Shahin Nazarian An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study
description	Abstract 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.
format	article
author	Zikun Yang Paul Bogdan Shahin Nazarian
author_facet	Zikun Yang Paul Bogdan Shahin Nazarian
author_sort	Zikun Yang
title	An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study
title_short	An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study
title_full	An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study
title_fullStr	An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study
title_full_unstemmed	An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study
title_sort	in silico deep learning approach to multi-epitope vaccine design: a sars-cov-2 case study
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/11a53413f913468ab9b7cb55f95e4071
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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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