Immunoinformatic approach to design a vaccine against SARS-COV-2 membrane glycoprotein

Abstract

SARS-COV-2 is a pandemic virus causing COVID-19 disease which affects lungs and upper respiratory tract leading to progressive increase in the death rate worldwide. Currently, there are more than 123 million cases and over 2.71 million confirmed death caused by this virus. In this study, by utilizing an immunoinformatic approach, multiepitope-based vaccine is designed from the membrane protein which plays a vital role in the virion assembly of the novel-CoV. A total of 19 MHC class- I binders with HLA-A and HLA-B alleles have been selected with NetMHC pan EL 4.0 method from IEDB MHC-I prediction server. Four epitopes candidates from M-protein were selected based on the antigenicity, stability, immunogenicity, Ramachandran plot and scores with 100 % was taken for docking analysis with alleles HLA-A (PDB ID: 1B0R) and HLA-B (PDB ID: 3C9N) using ClusPro server. Among the four epitopes, the epitope FVLAAVYRI has the least binding energy and forms electrostatic, hydrogen and hydrophobic interactions with HLA-A (-932.8 Kcal/mol) and HLA-B (-860.7 Kcal/mol) which induce the T-cell response. Each HLA-A and HLA-B complex in the system environment achieves stable backbone configuration between 45-100 ns of MD simulation. This study reports a potent antigenic and immunogenic profile of FVLAAVYRI epitope from M-protein and further in vitro and in vivo validation is needed for its adaptive use as vaccine against COVID-19.