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

Cao, K.; Li, R.; Strazar, M.; Brown, E. M.; Nguyen, P. N. U.; Pust, M.-M.; Park, J.; Graham, D. B.; Ashenberg, O.; Uhler, C.; Xavier, R.

doi:10.64898/2026.03.31.715361

bioRxiv · 10.64898/2026.03.31.715361

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

Cao, K.·Li, R.·Strazar, M.·Brown, E. M.·Nguyen, P. N. U.·Pust, M.-M.·Park, J.·Graham, D. B.·Ashenberg, O.·Uhler, C.·Xavier, R.

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Abstract

The interaction between T cell receptors (TCRs), peptides, and human leukocyte antigens (HLAs) underlies antigen-specific T cell immunity. Despite substantial advances in peptide- HLA presentation prediction, 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 both cancer and autoimmunity. As a case study in ankylosing spondylitis (AS), we screened 13 million peptides derived from 43,241 bacterial proteins and identified candidate molecular mimics that were experimentally validated to activate T cells expressing an AS-associated TCR. Notably, a top validated peptide was enriched in patients with inflammatory bowel disease (IBD), suggesting potential shared microbial triggers between AS and IBD. Overall, StriMap provides a generalizable framework for rational immunotherapy design and for dissecting antigenic drivers of autoimmunity.

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Cao, K., Li, R., Strazar, M., Brown, E. M., Nguyen, P. N. U., Pust, M.-M., Park, J., Graham, D. B., Ashenberg, O., Uhler, C., Xavier, R.. 2026-04-02. A structure-informed deep learning framework for modeling TCR-peptide-HLA interactions. https://doi.org/10.64898/2026.03.31.715361

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