Deep learning enhances the prediction of HLA class I-presented CD8⁺ T cell epitopes in foreign pathogens.

Abstract

Accurate in silico determination of CD8⁺ T cell epitopes would greatly enhance T cell-based vaccine development, but current prediction models are not reliably successful. Here, motivated by recent successes applying machine learning to complex biology, we curated a dataset of 651,237 unique human leukocyte antigen class I (HLA-I) ligands and developed MUNIS, a deep learning model that identifies peptides presented by HLA-I alleles. MUNIS shows improved performance compared with existing models in predicting peptide presentation and CD8⁺ T cell epitope immunodominance hierarchies. Moreover, application of MUNIS to proteins from Epstein-Barr virus led to successful identification of both established and novel HLA-I epitopes which were experimentally validated by in vitro HLA-I-peptide stability and T cell immunogenicity assays. MUNIS performs comparably to an experimental stability assay in terms of immunogenicity prediction, suggesting that deep learning can reduce experimental burden and accelerate identification of CD8⁺ T cell epitopes for rapid T cell vaccine development.