Exposing and exploiting host-parasite arms race clues in SARS-CoV-2: a principally new method for improved T cell immunogenicity prediction

Computational prediction of T cell epitopes is a crucial component in the development of novel vaccines. T cells in a healthy vertebrate host can recognize as non-self only those peptides that are present in the parasite's proteins but absent in the host's proteins. This principle enables us to determine the current and past host specificity of a parasite and to predict peptides capable of eliciting a T cell response. Building upon the detailed mapping of T cell clone specificity for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) antigens, we employed Monte Carlo tests to determine that empirically confirmed T cell-stimulating peptides have a significantly increased proportion of pentapeptides, hexapeptides and heptapeptides not found in the human proteome (P < 0.0001, Cohen's d > 4.9). We observed a lower density of potential pentapeptide targets for T cell recognition in the spike protein from the human-adapted SARS-CoV-2 ancestor compared to 10 other SARS-CoV-2 proteins originating from the horseshoe bat-adapted ancestor. Our novel method for predicting T cell immunogenicity of SARS-CoV-2 peptides is four times more effective than previous approaches. We recommend utilizing our theory-based method where efficient empirically based algorithms are unavailable, such as in the development of certain veterinary vaccines, and combining it with empirical methods in other cases for optimal results.

Automated summary

Computational prediction of T cell epitopes is crucial for vaccine development. Using Monte Carlo tests, we found that T cell-stimulating peptides have a significantly higher proportion of pentapeptides, hexapeptides, and heptapeptides not found in the human proteome. Our novel method for predicting T cell immunogenicity of SARS-CoV-2 peptides is four times more effective than previous approaches.