Prioritization of SARS-CoV-2 epitopes using a pan-HLA and global population inference approach

Abstract

SARS-CoV-2 T cell response assessment and vaccine development may benefit from an approach that considers the global landscape of the human leukocyte antigen (HLA) proteins. We predicted the binding affinity between 9-mer and 15-mer peptides from the SARS-CoV-2 peptidome for 9,360 class I and 8,445 class II HLA alleles, respectively. We identified 368,145 unique combinations of peptide-HLA complexes (pMHCs) with a predicted binding affinity less than 500nM, and observed significant overlap between class I and II predicted pMHCs. Using simulated populations derived from worldwide HLA frequency data, we identified sets of epitopes predicted in at least 90% of the population in 57 countries. We also developed a method to prioritize pMHCs for specific populations. Collectively, this public dataset and accessible user interface (Shiny app: https://rstudio-connect.parkerici.org/content/13/) can be used to explore the SARS-CoV-2 epitope landscape in the context of diverse HLA types across global populations.