Abstract
Understanding how T cells discriminate self from non-self is a fundamental question with important implications for immunology, immunotherapy, and vaccine development. Presentation of peptides by human leukocyte antigen I (HLA-I) molecules is necessary but not sufficient for T cell recognition, and peptide features that dictate immunogenicity are obscure. Here, we develop a convolutional neural network that learns features governing peptide immunogenicity, integrating molecular dynamics and sequence representations of humans, pathogen, and tumor peptides presented by HLA-I. Our model identified structural and dynamical properties correlated with immunogenicity and yielded a highly accurate classification of peptides from pathogens versus humans. Furthermore, we applied our model to classify more challenging cancer neoantigens, and it successfully predicted immunogenic neoepitopes from patients with melanomas. These data demonstrate the utility of deep learning models built on molecular dynamics and reveal underlying properties that govern HLA-I peptide immunogenicity.