{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Broad Institute of MIT and Harvard, Cambridge, MA USA",
    "Department of Molecular Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA USA",
    "Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA USA",
    "Massachusetts Institute of Technology, Cambridge, MA USA",
    "Center for the Study of Inflammatory Bowel Disease, Massachusetts General Hospital, Boston, MA USA"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Kai Cao"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Rui Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Martin Stražar",
      "orcid": "0000-0003-3064-1055"
    },
    {
      "affs": [
        0
      ],
      "name": "Eric M. Brown",
      "orcid": "0000-0001-8415-8021"
    },
    {
      "affs": [
        0
      ],
      "name": "Phuong N. U. Nguyen",
      "orcid": "0000-0003-4169-9658"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Marie-Madlen Pust",
      "orcid": "0000-0001-6870-2488"
    },
    {
      "affs": [
        0
      ],
      "name": "Jihye Park"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Daniel B. Graham",
      "orcid": "0000-0002-8403-8115"
    },
    {
      "affs": [
        0,
        2
      ],
      "name": "Orr Ashenberg"
    },
    {
      "affs": [
        0,
        3
      ],
      "name": "Caroline Uhler",
      "orcid": "0000-0002-7008-0216"
    },
    {
      "affs": [
        0,
        1,
        2,
        4
      ],
      "name": "Ramnik J. Xavier",
      "orcid": "0000-0002-5630-5167"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b7wxaahwg829h.bib",
  "cited_by": 3,
  "doi": "10.1038/s41467-026-78063-1",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13714282/",
  "id": "b7wxaahwg829h",
  "issns": [
    "2041-1723"
  ],
  "landing_url": "https://doi.org/10.1038/s41467-026-78063-1",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Article"
    ],
    [
      "openalex",
      "1312|vaccines and immunoinformatics approaches"
    ]
  ],
  "openalex": "W7214335917",
  "pdf_url": "https://www.nature.com/articles/s41467-026-78063-1_reference.pdf",
  "pmcid": "PMC13714282",
  "pmid": "42946765",
  "published": "2026-09-25",
  "publisher": "Nature Publishing Group",
  "ris": "https://research.draconitic.com/paper/b7wxaahwg829h.ris",
  "sources": [
    {
      "first_seen": "2026-11-01T12:21:48Z",
      "native_id": "PMC13714282",
      "source": "pmc",
      "url": "https://doi.org/10.1038/s41467-026-78063-1"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|vaccines and immunoinformatics approaches",
      "subfield": "molecular-biology"
    }
  ],
  "title": "A structure-informed deep learning framework for modeling TCR-peptide-HLA interactions",
  "title_text": "A structure-informed deep learning framework for modeling TCR-peptide-HLA interactions",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T12:21:48Z",
  "url": "https://research.draconitic.com/paper/b7wxaahwg829h",
  "venue": "Nature Communications",
  "versions": []
}