Immunoinformatics based prediction of recombinant multi-epitope vaccine for the control and prevention of SARS-CoV-2

The emergence of SARS-CoV-2 has been reported during December 2019, in the city of Wuhan, China. The transmission of this virus via human to human interaction has already been described. The novel virus has become pandemic and declared as a comprehensive emergency worldwide by World Health Organization due to its exponential spread within and outside China. There is a need of time to create a therapeutic agent and a vaccine to cure and control this lethal SARS-CoV-2. Conventionally, the vaccine development process is time taking, tiresome and requires more economical inputs with manpower. However, bioinformatics offers a key solution to compute the possibilities. The present study focuses on the utilization of bioinformatics platforms to forecast B and T cell epitopes that belong to SARS-CoV-2 spike glycoprotein. The protein is thought to have an involvement in triggering of momentous immune response. NCBI database was explored to collect the surface glycoprotein sequence and was analyzed to determine the immunogenic epitopes. This prediction analysis was carried out using IEDB web based server and the prediction of protein structure was done by homology modeling approach. This study resulted in prediction of 5T cell and 13B cell epitopes. Moreover, GPGPG linker was used to make these predicted epitopes a single peptide prior to further analysis. Afterwards, a 3D model of the final vaccine peptide was constructed, and the structure quality of the final construct was checked by Ramachandran Plot analysis and ProSA-web. Moreover, docking analysis highlighted three interactions of epitope against HLA-B7 including Lys 178, Gol 303 and Thr 31 residues. In conclusion, the predicted multi epitope peptide can be suggested as therapeutic or prophylactic candidate vaccine against SARS-CoV-2 after further confirmation by immunological assays.