An in-depth in silico and immunoinformatics approach for designing a potential multi-epitope construct for the effective development of vaccine to combat against SARS-CoV-2 encompassing variants of concern and interest.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the latest of the several viral pathogens that have acted as a threat to human health around the world. Thus, to prevent COVID-19 and control the outbreak, the development of vaccines against SARS-CoV-2 is one of the most important strategies at present. The study aimed to design a multi-epitope vaccine (MEV) against SARS-CoV-2. For the development of a more effective vaccine, 1549 nucleotide sequences were taken into consideration, including the variants of concern (B.1.1.7, B.1.351, P.1 and, B.1.617.2) and variants of interest (B.1.427, B.1.429, B.1.526, B.1.617.1 and P.2). A total of 11 SARS-CoV-2 proteins (S, N, E, M, ORF1ab polyprotein, ORF3a, ORF6, ORF7a, ORF7b, ORF8, ORF10) were targeted for T-cell epitope prediction and S protein was targeted for B-cell epitope prediction. MEV was constructed using linkers and adjuvant beta-defensin. The vaccine construct was verified, based on its antigenicity, physicochemical properties, and its binding potential, with toll-like receptors (TLR2, TLR4), ACE2 receptor and B cell receptor. The selected vaccine construct showed considerable binding with all the receptors and a significant immune response, including elevated antibody titer and B cell population along with augmented activity of TH cells, Tc cells and NK cells. Thus, immunoinformatics and in silico-based approaches were used for constructing MEV which is capable of eliciting both innate and adaptive immunity. In conclusion, the vaccine construct developed in this study has all the potential for the development of a next-generation vaccine which may in turn effectively combat the new variants of SARS-CoV-2 identified so far. However, in vitro and animal studies are warranted to justify our findings for its utility as probable preventive measure.