Skip to content
DraconiticResearch
Search Sources API
203,234 studies31 sourcesUpdated 22 min ago
Peer-reviewedCC BY-NC-ND 4.0Nature Communications

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

  • Kai Cao1
  • Rui Li1,2
  • Martin Stražar1
  • Eric M. Brown1
  • Phuong N. U. Nguyen1
  • Marie-Madlen Pust1,2
  • Jihye Park1
  • Daniel B. Graham1,2
  • Orr Ashenberg1,3
  • Caroline Uhler1,4
  • Ramnik J. Xavier1,2,3,5
5 affiliations
  1. 1Broad Institute of MIT and Harvard, Cambridge, MA USA
  2. 2Department of Molecular Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA USA
  3. 3Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA USA
  4. 4Massachusetts Institute of Technology, Cambridge, MA USA
  5. 5Center for the Study of Inflammatory Bowel Disease, Massachusetts General Hospital, Boston, MA USA
Read full textPDFDOICite

Abstract

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.

Related

  1. CPRISP: an interpretable deep learning framework predicting circRNA-RBP interactions and guiding therapeutic discovery in neurodegeneration
    Yongtian Wang, Jiahao Li, Xinmeng Liu +2
    Nature CommunicationsPeer-reviewedCC BY 4.0Molecular biology & gene regulationCrossref
  2. A Dynamics-Informed Machine Learning Framework for Inhibitor Optimization Against Quickly Evolving Targets to Avoid Resistance
    L. E. Intravaia, J. Kaur, A. M. Shaqra +7
    bioRxivPreprintCC BY-NC-NDFull textBiophysicsbioRxiv
  3. GeoEPred: A multimodal structure-aware geometric deep learning framework for Gram-negative bacterial secreted effector prediction with sequence semantics
    Shouzhen Song, Hua Shi, Hongfeng Wu +5
    PLOS Computational BiologyPeer-reviewedCC BY 4.0Full textMolecular biology & gene regulationGenetics & genomicsPLOS
  4. LEMON-ZEST: Evolution-Informed Tokenization for Efficient Protein Language Modeling
    Biswajit Banerjee, Claudia Alvarez Carreno, Anton S. Petrov
    arXivPreprintCC BY 4.0Full textBiophysicsarXiv
  5. Localized thermal perturbation alters lipid bilayer structure and daunorubicin interactions in MscL-containing membrane systems
    M. A. Dashti, D. Mohammad-Aghaie, O. Bavi
    Scientific ReportsPeer-reviewedCC BY-NC-ND 4.0Full textMolecular biology & gene regulationCrossref

Cite

BibTeXRIS
BibTeX
@article{cao2026structureinformed,
  title = {{A structure-informed deep learning framework for modeling TCR-peptide-HLA interactions}},
  author = {Kai Cao and Rui Li and Martin Stražar and Eric M. Brown and Phuong N. U. Nguyen and Marie-Madlen Pust and Jihye Park and Daniel B. Graham and Orr Ashenberg and Caroline Uhler and Ramnik J. Xavier},
  journal = {Nature Communications},
  year = {2026},
  month = sep,
  doi = {10.1038/s41467-026-78063-1},
  url = {https://doi.org/10.1038/s41467-026-78063-1},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - A structure-informed deep learning framework for modeling TCR-peptide-HLA interactions
AU  - Kai Cao
AU  - Rui Li
AU  - Martin Stražar
AU  - Eric M. Brown
AU  - Phuong N. U. Nguyen
AU  - Marie-Madlen Pust
AU  - Jihye Park
AU  - Daniel B. Graham
AU  - Orr Ashenberg
AU  - Caroline Uhler
AU  - Ramnik J. Xavier
PY  - 2026
DA  - 2026/09/25
JO  - Nature Communications
DO  - 10.1038/s41467-026-78063-1
UR  - https://doi.org/10.1038/s41467-026-78063-1
AB  - 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.
SN  - 2041-1723
ER  -