Natural Selection Footprint in Novel Coronavirus: A Genomic Perspective of SARS-COV2 Pandemic and Hypothesis for Peptide-Based Vaccine

Abstract

We retrospective analyzed in silico the binding affinity of SARS-CoV-2 peptides to MHC class I HLA-A, -B, and –C molecules in different countries with high and low morbidity and mortality rates. We used bioinformatics approach to screen 18260 SARS-CoV-2 epitopes that have significant affinity for different MHC class I alleles and found approximately five thousand predicted nonamers to bind different alleles. Those predicted epitopes show different significant affinity for frequently occurring MHC I alleles. regarding to HLA frequencies within different populations that can vary due to differences in their evolutionary histories, we showed that those alleles have different correlation with SARS-CoV-2 pandemic in 22 country based on different mortality and morbidity rate. There was a strong negative correlation between morbidity and mortality rates and the frequency of HLA-A*24, HLA-C*06 and HLA-B*5, while a strong positive correlation is detected between HLA-A*02, HLA-B*38, HLA-C*04 and HLA-C*08. We speculate that HLA class I polymorphism, by governing the set of viral peptides presented to CD8 + T cells, influences the outcome of SARS-Cov-2 infection. Finally, we were able to draw a foot print of natural selection on MHC I alleles base on significant different affinity of predicted peptide for known alleles. Our data showed that the HLA class I genetic background and the study epitope prediction should be taken into account for the generation of epitope-based vaccine or diagnostic tools. Funding: This work was supported by grants from the European Commission within the Horizon2020 Programmed TBVAC2020 [Horizon 2020 cod 643381]. Conflict of Interest: All the authors declare that no conflict of interests exist.