T cell receptor specificity landscape revealed through de novo peptide design
Year: 2025
Type: article
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.
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Awards: G4157101084, CAREER: Emergence of Functional Organization in the Adaptive Immune System, G4959173728, G5172056821, Kavli Institute for Theoretical Physics, Programs on Critical Problems in Physics, Astrophysics and Biophysics at the Aspen Center for Physics, G876022237, G9000081883, G9000260997, Decoding the interactions between T cell receptors and peptide-MHC
OpenAlex ID: W4415256372