A new and highly effective method for predicting T-cell response targets implemented on SARS-CoV-2 data

Abstract

Computational T-cell epitope prediction is essential in many immunological projects, including the development of vaccines. T-cells of immunocompetent vertebrate hosts can recognize as non-self only peptides which are present in the parasites proteins and absent in the hosts proteins. This basic principle allows us to predict which peptides can elicit T-cells response. We built on the fact that the specificity of T-cells reacting to SARS-CoV-2 antigens has been recently mapped in detail. Using Monte Carlo tests, we found that empirically confirmed peptides that stimulate T-cells contain an increased fraction of pentapeptides, hexapeptides, and heptapeptides which are not found in the human proteome (p < 0.0001). Similarly, hexapeptides absent in human proteins were overrepresented in peptides that elicited T-cell response in a published empirical study (p = 0.027). The new theory-based method predicted T-cell immunogenicity of SARS-CoV-2 peptides four times more effectively than current empirically based methods.