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COVID-19 is an infectious disease caused by a newly discovered corona virus SARS-COV-2. It is the most dangerous epidemic existing currently all over the world. To date, there is no licensed vaccine and not any particular efficient therapeutic agent available to prevent or cure the disease. So development of an effective vaccine is the urgent need of the time. The proposed study aims to identify potential vaccine candidates by screening the complete proteome of SARS-COV-2 using the computational approach. From 14 protein entries in UniProtKB, 4 proteins were screened for epitope prediction based on consensus antigenicity predictions and various physico-chemical criteria like transmembrane domain, allergenicity, GRAVY value, toxicity, stability index. Comprehensive analysis of these 4 antigens revealed that spike protein (P0DTC2) and nucleoprotein (P0DTC9) show the greatest potential for experimental immunogenicity analysis. These 2 proteins have several potential CD4+ and CD8+ T-cell epitopes, as well as high probability of B-cell epitope regions as compared to well-characterized antigen the matrix protein 1 [Influenza A virus (H5N1)]. In addition, the epitope SIIAYTMSL predicted from spike protein (P0DTC2) and epitope SPRWYFYYL predicted from nucleoprotein (P0DTC9) exhibited more than 60% population coverage in the target populations Europe, North America, South Asia, Northeast Asia taken in this study. These epitopes have also been found to exhibit highly significant TCR-pMHC interactions having a joint Z value of 4.51 and 4.37 respectively. Therefore, this analysis suggests that the predicted epitopes might be suitable vaccine candidates and should be subjected to further in-vivo and in-vitro studies.

Prediction of Epitope based Peptides for Vaccine Development from Complete Proteome of Novel Corona Virus (SARS-COV-2) Using Immunoinformatics