T cell receptor specificity landscape revealed through de novo peptide design.

Abstract

T cells play a key role in adaptive immunity by mounting specific responses against diverse pathogens. Effective bindings between T cell receptors (TCRs) and pathogen derived peptides presented on major histocompatibility complexes (MHCs) mediate immune responses. However, predicting these interactions remains challenging due to limited functional data on T cell reactivities. Here, we introduce a computational approach to predict TCR interactions with peptides presented on MHC-I alleles, and to design immunogenic peptides for specified TCR-MHC complexes. Our method leverages HERMES, a structure-based machine learning model trained on the protein universe to predict amino acid preferences based on local structural environments. Despite no direct training on TCR-pMHC data, HERMES's implicit physical reasoning enables us to make accurate predictions of both TCR-pMHC binding affinities and T cell activities across diverse viral and cancer epitopes, achieving up to 0.72 correlation with experimental data. Leveraging our TCR recognition model, we develop a computational protocol for de novo design of immunogenic peptides. Through experimental validation in three TCR-MHC systems, we demonstrate that our designs-with up to five substitutions from the native sequence-activate T cells at success rates of up to 50%. Last, we use our generative framework to quantify the diversity of the peptide recognition landscape for various TCR-MHC's, offering key insights into T cell specificity. Our approach provides a platform for immunogenic peptide and neoantigen design, as well as for evaluating TCR specificity, offering a computational framework to inform design of engineered T cell therapies and vaccines.