In silico design of multiepitope vaccine candidate against SARS CoV and CoV2

SARS-CoV-2 infection became pandemic and public concern due to the outbreak of COVID-19. Since its appearance in Wuhan,
China in December 2019 the infection spread across continents. Substantial numbers of global population were disrupted both in their health and family structure. The spike protein is found to be the key protein of coronavirus as it anchors to human alveolar epi-thelial cells. Hence, the present study is focused on immune informatics analysis of spike glycoprotein (S-protein) of SARS-CoV-2 to explore MHC binding potential of its epitopes. Out of 5058 peptides, each with nine residues obtained through NetMHCpan version 4.1b, six peptides were predicted as epitopes with strong binding potential to HLA-A*02:01, whose distribution is ~33.8% among Indian Asians. Among these six peptides, the amino acid sequence starting from 417th position namely KIADYNYKL was found to be highly probable antigen with a value of 1.6639 derived through VaxiJen V2.0. Peptides predicted in the receptor binding region also revealed that the aforementioned peptide ranked as the strong binder to HLA-A*02:01. The secondary structure prediction of RBD performed through Phyre2 revealed that these peptide residues spanned to stretch in beta strand followed by random coil and reported as immunodominant region using Immunome Browser tool. This study would pave the path to understand the prevalent
Delta Plus variant of CoV2 (K417N) and facilitates to design a recombinant vaccine.