MORITS: An improved method to predict peptides from heterologous proteins that are recognized by the same T-cell receptor

Abstract

AbstractAntigen-specific priming of T cells results in the activation of T cells that exert effector functions by interaction of their T-cell receptor (TCR) with the corresponding self-MHC molecule presenting a peptide on the surface of a target cell. Such antigen-specific T cells potentially can also interact with peptide-MHC complexes that contain peptides from unrelated antigens, a phenomenon that often is referred to as heterologous immunity. For example, some individuals that were pre-immunized against an allergen, could subsequently mount better anti-viral T-cell responses than non-allergic individuals. So far only few peptide pairs that experimentally have been shown to provoke heterologous immunity were  identified, and available prediction tools that can identify potential candidates are imprecise. We developed the MORITS algorithm to rapidly screen large lists of peptides for sequence similarities, while giving enhanced consideration to peptide residues presented by MHC that are particularly relevant for TCR interactions. In combination with established peptide-MHC binding prediction tools, the MORITS algorithm revealed peptide similarities between the SARS-CoV-2 proteome and certain allergens. The method outperformed previously published workflows and may help to identify novel pairs of peptides that mediate heterologous immune responses.