Dino Wu, Nathaniel Park, et al.
ACS Fall 2022
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 molecular features that dictate HLA-peptide complex immunogenicity are obscure. Here, we apply a distance-weighted graph convolutional neural network that learns features governing peptide immunogenicity from molecular coordinates, integrating thousands of molecular dynamics simulations of human and pathogen peptides presented by HLA-A*02:01. Our model identifies structural and dynamical properties correlated with immunogenicity and yields a highly accurate classification of peptides from pathogens versus humans. These data demonstrate the potential utility of deep learning models built on molecular dynamics to reveal underlying properties that govern HLA-I peptide immunogenicity.
Dino Wu, Nathaniel Park, et al.
ACS Fall 2022
Ana Stanojevic, Stanisław Woźniak, et al.
Nature Communications
Matheus Esteves Ferreira, Jaione Tirapu Azpiroz, et al.
ACS Fall 2022
Yidi Wu, Thomas Bohnstingl, et al.
ICML 2025