Millions of individuals have been affected by the novel coronavirus (SARS-CoV-2) pandemic, which has resulted in a large number of deaths throughout the world. Because of the virus's rapid virulence, SARS-CoV-2 treatments were highly restricted. Hence, vaccines were urgently needed, as there were no viable medical treatments. In this study, immunoinformatics methods were used to create a multi-epitope vaccine with the chance of activating the human immune system against SARS-CoV-2. The first group of epitopes was filtered from the viral structural protein. To identify potential antigenic, non-toxic and non-allergenic T-cell and B-cell epitopes vaxijen2.0, allertop v2.0, and toxinpred was utilized and a predicted model was generated. Cytokine inducing epitopes were evaluated using IFNepitope, IL4pred, and IL10pred. For T-cell, one MHC I (VSLVKPSFY) binding cytotoxic T lymphocyte (CTL) (9-mer) and MHC II (VYSRVKNLNSSRVPD) binding helper T lymphocyte (HTL) (9-mer) was evaluated as both have strong binding affinity and were antigenic with a score of 0.7476 and 0.5993 respectively. The HTL epitope was capable of inducing interferon-gamma, interleukin-4 and interleukin-10. The selected B-cell epitope (YVYSRVKNLNSSRVP) with a length of 15 and antigen score of 0.4992 was non-toxic and non-allergenic as well. With the use of suitable linkers, epitopes were then linked together and biochemical analysis in PROTPARAM showed the constructed vaccine’s instability index (44.39) and GRAVY (-0.023). Through homology modeling, the Phyre2 server predicted a PDB model of the final vaccine which showed 99.8% confidence with coverage of 43%. ProSA web was used to evaluate the z score (0.5) for finding the overall model quality. Patchdock showed the molecular docking score of 19382, using shape complementary concepts in 2594.60 square angstrom area. The immunogenic profile of the proposed vaccine was analyzed using the C-IMMSIM server.

Design of a multi-epitope-based peptide vaccine against envelope protein (E) of SARS-CoV-2: an <i>in silico</i> approach

ACS Spring 2022

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Design of a multi-epitope-based peptide vaccine against envelope protein (E) of SARS-CoV-2: an in silico approach