Identification of Potential Immunogenic Epitopes against SARS-CoV-2 using In-Silico Method: An Immunoinformatics Study

Abstract

Severe Acute Respiratory Syndrome (SARS-CoV-2), a zoonotic virus, is the pathogenic causal agent for the ongoing pandemic. Despite the lethality of the disease, there are no therapeutic agents available to combat the disease outbreak; and the vaccines currently accessible are insufficient to control the widespread, fast-mutating virus infection. This research study focuses on determining potential epitopes by examining the entire proteome of the SARS-CoV-2 virus using an in-silico approach. To design a vaccine for the deadly virus, the entire proteome of the SARS-CoV-2 virus was screened for identification of potential epitopes in order to identify the potent peptide candidate which is both unique and simultaneously solves the purpose of the vaccine discovery. It is mandatory to identify the suitable B-cell and T-cell epitopes of the observed SARS-CoV-2 Surface Glycoprotein (QKN61229.1). These epitopes were subjected to various tests, including antigenicity, allergenicity, and other physicochemical properties. The T-cell epitopes that met all of the criteria were then subjected to Population Coverage Analysis. It helped better understand the response of epitopes to the target population, compute the conservancy of a peptide, and then cluster them based on their sequence match, MHC binding, and T-cell restriction sites. Lastly, the interactions between the T-Cell Receptor (TCR) and a peptide-MHC were studied to gain a thorough understanding of MHC-restriction to design a peptide-vaccine. The results showed that there were 4 B-Cell epitopes, 2 MHC-I epitopes, 4 MHC-II epitopes that qualified all the subjected tests and thus have an affinity to prominent antigens. ased on the results obtained from this study, the estimated peptides are a promising candidate for peptide-vaccine design and development.