In silico Investigation of Immunodominant Antigenic Regions, Helper T Lymphocyte, and Cytotoxic T Lymphocyte Epitopes Credentials for SARS-CoV-2 Vaccination

Abstract

Background: In recent days, COVID-19 cases are increasing globally at an alarming rate due to the COVID-19 second wave despite the mass vaccination programs. Search for the potential vaccine for SARS-CoV-2 is still under progress. The epitope-based vaccine is effective and is a cornerstone in vaccine development. The quick prediction of epitopes could be a proficient way to monitor vaccine development during a global health crisis. Objective: This study is designed to predict the potential epitopes with computational tools for vaccine development. Methods: NetCTLpan v. 1.1 and NetMHCIIpan v. 3.2 servers were used for T-cell epitope analysis. IEDB servers were employed for HLA and DRB1 peptide calculations. The epitope Immunogenicity, toxicity, physiochemical character, and other features are measured by immunogen evaluation. Furthermore, the top-ranked immunogenic epitopes were computationally validated by molecular docking analysis. The epitopes are docked to Toll-like receptors (TLRs), which is helpful to generate an immune response against SARS-CoV-2. Results: Overall, six HTL and CTL epitopes were predicted (IDGYFKIYSKH, HPLSHFVNLDNL, RIGNNYKLNT, and WTAGAAAYYVG, MACLVGLMWLS, FRLKGGAPIKGVT), which had good immunogenicity scores, and stable interaction with Toll-like receptor (TLR). Therefore, these epitopes can bind with HLA and DRB1 molecules, respectively. Conclusion: The computationally predicted antigenic regions might be considered for epitope-based vaccine against SARS-CoV-2 after in vitro