A structure-informed deep learning framework for modeling TCR-peptide-HLA interactions

Interactions between T cell receptors (TCRs), peptides, and human leukocyte antigens (HLAs) underlie antigen-specific T cell immunity. Despite substantial advances in prediction methods, accurate modeling of coupled TCR-peptide-HLA recognition remains underdeveloped, limiting applications such as TCR and neoepitope prioritization in cancer and antigen identification in autoimmunity. Here we present StriMap, a unified framework for predicting TCR-peptide-HLA interactions by integrating physicochemical, sequence-context, and structural features at recognition interfaces. StriMap achieves state-of-the-art performance with improved generalizability and enables applications in cancer and autoimmunity. As a case study, we screened 13 million peptides from 43,241 bacterial proteins and identified candidate molecular mimics that were experimentally validated to activate T cells expressing an ankylosing spondylitis (AS)-associated TCR. A top validated peptide was enriched in patients with inflammatory bowel disease (IBD), suggesting potential shared microbial triggers. Overall, StriMap provides a framework for rational immunotherapy design and dissecting antigenic drivers of autoimmunity.