The rapid global spread and impact of SARS-CoV-2 and other emerging infectious diseases highlight the urgent need for the swift development of effective vaccines. Traditional vaccine production processes are time-consuming and resource-intensive. The present study focused on an in-silico strategy combined with sophisticated bioinformatics tools to develop a multi-epitope vaccine candidate against the SARS-CoV-2 membrane glycoprotein. The full SARS-CoV-2 genome was downloaded from NCBI to determine the sequence of the membrane glycoprotein for further research. The selected protein was subjected to antigenicity and non-allergenicity testing with the assistance of computational tools. Immuno-informatics software was used to predict B-cell and T-cell epitopes, like MHC-I and MHC-II sequences. The predicted epitopes' binding affinity to the respective MHC alleles and their ability to cover different populations were evaluated. Through a systematic in silico approach, we identified potential immunogenic epitopes that resulted in constructing a multi-epitope vaccine construct to generate strong immune responses. The present research demonstrates how bioinformatics accelerates vaccine development while minimizing the need for extensive initial experimental validation.
