Designing a multi-epitope candidate vaccine against SARS-CoV-2 through in silico approach for producing in plant systems

Abstract

The COVID-19 is considered as a type of severe acute respiratory syndrome (SARS-CoV-2). The current pandemic causes a vital destruction in international social and economic systems. Current available vaccines involve entire viruses; however, peptide-based vaccines could be also beneficial. In the present study, a computationally candidate vaccine was designed against SARS-CoV-2. Surface glycoproteins (E, M, and S proteins) and N protein amino acid sequences were analyzed to predict high score of the B and T cell epitopes as antigenic proteins of the virus. High score epitopes, and the B subunit of Vibrio cholerae toxin, as an adjuvant put together by appropriate linkers to construct a multi-epitope candidate vaccine. Bioinformatics tools were used to predict the secondary, tertiary structure and physicochemical properties, such as aliphatic index, theoretical pH, molecular weight, and estimated half-life of the multi-epitope candidate vaccine. The interaction of candidate vaccine with TLR2 and TLR4 was computationally evaluated by molecular docking. Finally, the codon optimization and the secondary structure of mRNA were calculated, and in silico cloning was performed into plant expression vector by SnapGENE. This designed candidate vaccine along with the computational results requires laboratory evaluations to be confirmed as a candidate vaccine against SARS-COV-2 infection. Keywords: COVID-19, SARS-CoV-2, in silico, Multi-epitope candidate vaccine, Plant systems.