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Despite extensive structural characterization of TCR\u2013pMHC complexes, the molecular principles underlying this process remain incompletely understood, hindered by the inherent duality of TCR specificity and cross-reactivity. Traditional structural analyses often fall short in capturing the multidimensional features that govern TCR\u2013pMHC engagement. Here, we introduce a multimodal geometric deep learning framework that systematically extracts and learns various physicochemical and spatial features from pMHC interfaces, which encode key immunological cues for TCR recognition. Applied to a curated dataset of human leukocyte antigens HLA-A*02\u2013peptide\u2013TCR crystal structures, our model robustly predicts TCR binding preferences and uncovers interfacial \u201cimmunological fingerprints\u201d that inform receptor engagement. Through an integrated explainability module, we identify critical contact residues and interaction motifs, thus providing interpretable insights into the determinants of TCR specificity. We further demonstrate the model\u2019s generalizability by analyzing HLA-B*27\u2013peptide complexes, revealing potential TCR cross-reactivity between self-derived and bacterial peptides\u2014highlighting its utility in probing molecular mimicry. This work establishes a scalable, structure-based approach for decoding T cell recognition and offers a powerful tool for guiding antigen design, vaccine development, and TCR-based immunotherapies.<\/jats:p>","DOI":"10.1093\/bib\/bbag048","type":"journal-article","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T15:38:25Z","timestamp":1773329905000},"source":"Crossref","is-referenced-by-count":0,"title":["Decoding TCR recognition via geometric deep learning of immunological fingerprints"],"prefix":"10.1093","volume":"27","author":[{"given":"Chun","family":"Shang","sequence":"first","affiliation":[{"name":"College of Physics, College of Life Sciences, and Institute of Quantitative Biology, Zhejiang University , 866 Yuhangtang Road, Hangzhou 310058,","place":["China"]},{"name":"Shanghai Institute for Advanced Study, Zhejiang University , 799 Dangui Road, Shanghai 201203,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3705-1835","authenticated-orcid":false,"given":"Kevin C","family":"Chan","sequence":"additional","affiliation":[{"name":"Department of Biosciences and Bioinformatics, School of Science, Xi\u2019an Jiaotong\u2013Liverpool University , 111 Ren\u2019ai Road, Suzhou 215123,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8624-5591","authenticated-orcid":false,"given":"Ruhong","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Physics, College of Life Sciences, and Institute of Quantitative Biology, Zhejiang University , 866 Yuhangtang Road, Hangzhou 310058,","place":["China"]},{"name":"Shanghai Institute for Advanced Study, Zhejiang University , 799 Dangui Road, Shanghai 201203,","place":["China"]},{"name":"The First Affiliated Hospital, School of Medicine, Zhejiang University , 866 Yuhangtang Road, Hangzhou 310058,","place":["China"]},{"name":"Department of Chemistry, Columbia University , New York, NY 10027 ,","place":["United States"]}]}],"member":"286","published-online":{"date-parts":[[2026,3,16]]},"reference":[{"key":"2026031601112765600_ref1","doi-asserted-by":"publisher","first-page":"744","DOI":"10.1038\/335744b0","article-title":"T-cell antigen receptor genes and T-cell recognition","volume":"335","author":"Davis","year":"1988","journal-title":"Nature"},{"key":"2026031601112765600_ref2","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1038\/nri2887","article-title":"Mechanisms for T cell receptor triggering","volume":"11","author":"Merwe","year":"2011","journal-title":"Nat Rev Immunol"},{"key":"2026031601112765600_ref3","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1146\/annurev.immunol.17.1.369","article-title":"Structural basis of T cell recognition","volume":"17","author":"Garcia","year":"1999","journal-title":"Annu Rev 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