{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T17:56:13Z","timestamp":1774288573023,"version":"3.50.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,2,26]],"date-time":"2025-02-26T00:00:00Z","timestamp":1740528000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,2,26]],"date-time":"2025-02-26T00:00:00Z","timestamp":1740528000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62306333"],"award-info":[{"award-number":["62306333"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62025208"],"award-info":[{"award-number":["62025208"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32070025"],"award-info":[{"award-number":["32070025"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2023YFC260500"],"award-info":[{"award-number":["2023YFC260500"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Nat Mach Intell"],"DOI":"10.1038\/s42256-025-01002-0","type":"journal-article","created":{"date-parts":[[2025,2,26]],"date-time":"2025-02-26T10:05:03Z","timestamp":1740564303000},"page":"650-660","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["A unified deep framework for peptide\u2013major histocompatibility complex\u2013T cell receptor binding prediction"],"prefix":"10.1038","volume":"7","author":[{"given":"Yunxiang","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Jijun","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Yixin","family":"Su","sequence":"additional","affiliation":[]},{"given":"You","family":"Shu","sequence":"additional","affiliation":[]},{"given":"Enhao","family":"Ma","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9413-2378","authenticated-orcid":false,"given":"Jing","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Shuyang","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Congwen","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Dongsheng","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4819-373X","authenticated-orcid":false,"given":"Zhen","family":"Huang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7447-5488","authenticated-orcid":false,"given":"Gong","family":"Cheng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9151-6090","authenticated-orcid":false,"given":"Hongguang","family":"Ren","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9322-549X","authenticated-orcid":false,"given":"Jiannan","family":"Feng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,26]]},"reference":[{"key":"1002_CR1","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1038\/s41577-020-0306-5","volume":"20","author":"AD Waldman","year":"2020","unstructured":"Waldman, A. D., Fritz, J. M. & Lenardo, M. J. A guide to cancer immunotherapy: from T cell basic science to clinical practice. Nat. Rev. Immunol. 20, 651\u2013668 (2020).","journal-title":"Nat. Rev. Immunol."},{"key":"1002_CR2","doi-asserted-by":"publisher","first-page":"1488","DOI":"10.1038\/s41591-019-0596-y","volume":"25","author":"TN Yamamoto","year":"2019","unstructured":"Yamamoto, T. N., Kishton, R. J. & Restifo, N. P. Developing neoantigen-targeted T cell\u2013based treatments for solid tumors. Nat. Med. 25, 1488\u20131499 (2019).","journal-title":"Nat. Med."},{"key":"1002_CR3","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1038\/s42256-023-00634-4","volume":"5","author":"X Peng","year":"2023","unstructured":"Peng, X. et al. Characterizing the interaction conformation between T-cell receptors and epitopes with deep learning. Nat. Mach. Intell. 5, 395\u2013407 (2023).","journal-title":"Nat. Mach. Intell."},{"key":"1002_CR4","doi-asserted-by":"publisher","first-page":"bbaa415","DOI":"10.1093\/bib\/bbaa415","volume":"22","author":"S Mei","year":"2021","unstructured":"Mei, S. et al. Anthem: a user customised tool for fast and accurate prediction of binding between peptides and HLA class I molecules. Brief. Bioinform. 22, bbaa415 (2021).","journal-title":"Brief. Bioinform."},{"key":"1002_CR5","doi-asserted-by":"publisher","first-page":"1119","DOI":"10.1093\/bib\/bbz051","volume":"21","author":"S Mei","year":"2020","unstructured":"Mei, S. et al. A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction. Brief. Bioinform. 21, 1119\u20131135 (2020).","journal-title":"Brief. Bioinform."},{"key":"1002_CR6","doi-asserted-by":"publisher","unstructured":"Fast, E., Dhar, M. & Chen, B. TAPIR: a T-cell receptor language model for predicting rare and novel targets. Preprint at bioRxiv https:\/\/doi.org\/10.1101\/2023.09.12.557285 (2023).","DOI":"10.1101\/2023.09.12.557285"},{"key":"1002_CR7","doi-asserted-by":"publisher","first-page":"492","DOI":"10.1038\/s42003-023-04867-2","volume":"6","author":"M Kalemati","year":"2023","unstructured":"Kalemati, M., Darvishi, S. & Koohi, S. CapsNet-MHC predicts peptide-MHC class I binding based on capsule neural networks. Commun. Biol. 6, 492 (2023).","journal-title":"Commun. Biol."},{"key":"1002_CR8","doi-asserted-by":"publisher","first-page":"4946","DOI":"10.1093\/bioinformatics\/btz427","volume":"35","author":"Y Hu","year":"2019","unstructured":"Hu, Y. et al. ACME: pan-specific peptide-MHC class I binding prediction through attention-based deep neural networks. Bioinformatics 35, 4946\u20134954 (2019).","journal-title":"Bioinformatics"},{"key":"1002_CR9","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1093\/nar\/gkaa379","volume":"48","author":"B Reynisson","year":"2020","unstructured":"Reynisson, B., Alvarez, B., Paul, S., Peters, B. & Nielsen, M. NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic Acids Res. 48, 449\u2013454 (2020).","journal-title":"Nucleic Acids Res."},{"key":"1002_CR10","doi-asserted-by":"publisher","first-page":"866","DOI":"10.1002\/prot.26065","volume":"89","author":"J Jin","year":"2021","unstructured":"Jin, J. et al. Deep learning pan-specific model for interpretable MHC-I peptide binding prediction with improved attention mechanism. Proteins: Struct., Funct., Bioinform. 89, 866\u2013883 (2021).","journal-title":"Proteins: Struct., Funct., Bioinform."},{"key":"1002_CR11","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1038\/s42256-022-00459-7","volume":"4","author":"Y Chu","year":"2022","unstructured":"Chu, Y. et al. A transformer-based model to predict peptide-HLA class I binding and optimize mutated peptides for vaccine design. Nat. Mach. Intell. 4, 300\u2013311 (2022).","journal-title":"Nat. Mach. Intell."},{"key":"1002_CR12","doi-asserted-by":"publisher","first-page":"bbac173","DOI":"10.1093\/bib\/bbac173","volume":"23","author":"Y Zhang","year":"2022","unstructured":"Zhang, Y. et al. HLAB: learning the BiLSTM features from the ProtBert-encoded proteins for the class I HLA-peptide binding prediction. Brief. Bioinform. 23, bbac173 (2022).","journal-title":"Brief. Bioinform."},{"key":"1002_CR13","doi-asserted-by":"publisher","first-page":"5835","DOI":"10.1126\/sciadv.abf5835","volume":"7","author":"W Zhang","year":"2021","unstructured":"Zhang, W. et al. A framework for highly multiplexed dextramer mapping and prediction of T cell receptor sequences to antigen specificity. Sci. Adv. 7, 5835 (2021).","journal-title":"Sci. Adv."},{"key":"1002_CR14","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1038\/s42256-023-00619-3","volume":"5","author":"Y Gao","year":"2023","unstructured":"Gao, Y. et al. Pan-peptide meta learning for T-cell receptor\u2013antigen binding recognition. Nat. Mach. Intell. 5, 236\u2013249 (2023).","journal-title":"Nat. Mach. Intell."},{"key":"1002_CR15","doi-asserted-by":"publisher","first-page":"864","DOI":"10.1038\/s42256-021-00383-2","volume":"3","author":"T Lu","year":"2021","unstructured":"Lu, T. et al. Deep learning-based prediction of the T cell receptor\u2013antigen binding specificity. Nat. Mach. Intell. 3, 864\u2013875 (2021).","journal-title":"Nat. Mach. Intell."},{"key":"1002_CR16","doi-asserted-by":"publisher","first-page":"100423","DOI":"10.1016\/j.cosrev.2021.100423","volume":"42","author":"P De Handschutter","year":"2021","unstructured":"De Handschutter, P., Gillis, N. & Siebert, X. A survey on deep matrix factorizations. Comput. Sci. Rev. 42, 100423 (2021).","journal-title":"Comput. Sci. Rev."},{"key":"1002_CR17","unstructured":"Oord, A.v.d., Li, Y. & Vinyals, O. Representation learning with contrastive predictive coding. Preprint at https:\/\/arxiv.org\/abs\/1807.03748 (2018)."},{"key":"1002_CR18","first-page":"5586","volume":"34","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y. & Yang, Q. A survey on multi-task learning. IEEE Trans. Knowl. Data Eng. 34, 5586\u20135609 (2021).","journal-title":"IEEE"},{"key":"1002_CR19","doi-asserted-by":"publisher","unstructured":"Lin, Z. et al. Language models of protein sequences at the scale of evolution enable accurate structure prediction. Preprint at bioRxiv https:\/\/doi.org\/10.1101\/2022.07.20.500902 (2022).","DOI":"10.1101\/2022.07.20.500902"},{"key":"1002_CR20","unstructured":"Hamilton, W., Ying, Z. & Leskovec, J. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems 30, 1025\u20131035 (Curran Associates, 2017)."},{"key":"1002_CR21","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-37214-1","volume":"9","author":"Z Liu","year":"2019","unstructured":"Liu, Z. et al. DeepSeqPan, a novel deep convolutional neural network model for pan-specific class I HLA-peptide binding affinity prediction. Sci. Rep. 9, 794 (2019).","journal-title":"Sci. Rep."},{"key":"1002_CR22","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1093\/nar\/gky1006","volume":"47","author":"R Vita","year":"2019","unstructured":"Vita, R. et al. The Immune Epitope Database (IEDB): 2018 update. Nucleic Acids Res. 47, 339\u2013343 (2019).","journal-title":"Nucleic Acids Res."},{"key":"1002_CR23","doi-asserted-by":"publisher","unstructured":"Jensen, M.F. & Nielsen, M. NetTCR 2.2\u2014improved TCR specificity predictions by combining pan-and peptide-specific training strategies, loss-scaling and integration of sequence similarity. Preprint at bioRxiv https:\/\/doi.org\/10.1101\/2023.10.12.562001 (2023).","DOI":"10.1101\/2023.10.12.562001"},{"key":"1002_CR24","doi-asserted-by":"publisher","first-page":"bbaa318","DOI":"10.1093\/bib\/bbaa318","volume":"22","author":"P Moris","year":"2021","unstructured":"Moris, P. et al. Current challenges for unseen-epitope TCR interaction prediction and a new perspective derived from image classification. Brief. Bioinform. 22, bbaa318 (2021).","journal-title":"Brief. Bioinform."},{"key":"1002_CR25","doi-asserted-by":"publisher","first-page":"1060","DOI":"10.1038\/s42003-021-02610-3","volume":"4","author":"A Montemurro","year":"2021","unstructured":"Montemurro, A. et al. NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCR\u03b1 and \u03b2 sequence data. Commun. Biol. 4, 1060 (2021).","journal-title":"Commun. Biol."},{"key":"1002_CR26","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-21879-w","volume":"12","author":"J-W Sidhom","year":"2021","unstructured":"Sidhom, J.-W., Larman, H. B., Pardoll, D. M. & Baras, A. S. DeepTCR is a deep learning framework for revealing sequence concepts within T-cell repertoires. Nat. Commun. 12, 1605 (2021).","journal-title":"Nat. Commun."},{"key":"1002_CR27","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1038\/nature22383","volume":"547","author":"P Dash","year":"2017","unstructured":"Dash, P. et al. Quantifiable predictive features define epitope-specific T cell receptor repertoires. Nature 547, 89\u201393 (2017).","journal-title":"Nature"},{"key":"1002_CR28","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.it.2004.12.003","volume":"26","author":"CA Klebanoff","year":"2005","unstructured":"Klebanoff, C. A., Khong, H. T., Antony, P. A., Palmer, D. C. & Restifo, N. P. Sinks, suppressors and antigen presenters: how lymphodepletion enhances T cell-mediated tumor immunotherapy. Trends Immunol. 26, 111\u2013117 (2005).","journal-title":"Trends Immunol."},{"key":"1002_CR29","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1038\/nature22976","volume":"547","author":"J Glanville","year":"2017","unstructured":"Glanville, J. et al. Identifying specificity groups in the T cell receptor repertoire. Nature 547, 94\u201398 (2017).","journal-title":"Nature"},{"key":"1002_CR30","doi-asserted-by":"publisher","first-page":"12704","DOI":"10.1073\/pnas.1809642115","volume":"115","author":"MV Pogorelyy","year":"2018","unstructured":"Pogorelyy, M. V. et al. Precise tracking of vaccine-responding T cell clones reveals convergent and personalized response in identical twins. Proc. Natl Acad. Sci. USA 115, 12704\u201312709 (2018).","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"1002_CR31","doi-asserted-by":"publisher","first-page":"8995","DOI":"10.1073\/pnas.1902649116","volume":"116","author":"H Huang","year":"2019","unstructured":"Huang, H. et al. Select sequencing of clonally expanded CD8+ T cells reveals limits to clonal expansion. Proc. Natl Acad. Sci. USA 116, 8995\u20139001 (2019).","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"1002_CR32","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1038\/s42003-024-05968-2","volume":"7","author":"P Borole","year":"2024","unstructured":"Borole, P. & Rajan, A. Building trust in deep learning-based immune response predictors with interpretable explanations. Commun. Biol. 7, 279 (2024).","journal-title":"Commun. Biol."},{"key":"1002_CR33","doi-asserted-by":"publisher","first-page":"1009736","DOI":"10.1371\/journal.pcbi.1009736","volume":"18","author":"Q Dickinson","year":"2022","unstructured":"Dickinson, Q. & Meyer, J. G. Positional SHAP (PoSHAP) for interpretation of machine learning models trained from biological sequences. PLoS Comput. Biol. 18, 1009736 (2022).","journal-title":"PLoS Comput. Biol."},{"key":"1002_CR34","doi-asserted-by":"publisher","first-page":"819583","DOI":"10.3389\/fbioe.2022.819583","volume":"10","author":"J Yu","year":"2022","unstructured":"Yu, J. et al. CAD v1.0: Cancer Antigens Database platform for cancer antigen algorithm development and information exploration. Front. Bioeng. Biotechnol. 10, 819583 (2022).","journal-title":"Front. Bioeng. Biotechnol."},{"key":"1002_CR35","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1038\/s42256-023-00694-6","volume":"5","author":"BA Albert","year":"2023","unstructured":"Albert, B. A. et al. Deep neural networks predict class I major histocompatibility complex epitope presentation and transfer learn neoepitope immunogenicity. Nat. Mach. Intell. 5, 861\u2013872 (2023).","journal-title":"Nat. Mach. Intell."},{"key":"1002_CR36","doi-asserted-by":"publisher","first-page":"bbab335","DOI":"10.1093\/bib\/bbab335","volume":"22","author":"Z Xu","year":"2021","unstructured":"Xu, Z. et al. DLpTCR: an ensemble deep learning framework for predicting immunogenic peptide recognized by T cell receptor. Brief. Bioinform. 22, bbab335 (2021).","journal-title":"Brief. Bioinform."},{"key":"1002_CR37","doi-asserted-by":"publisher","unstructured":"Jurtz, V. et al. NetTCR: sequence-based prediction of TCR binding to peptide-MHC complexes using convolutional neural networks. Preprint at bioRxiv https:\/\/doi.org\/10.1101\/433706 (2018).","DOI":"10.1101\/433706"},{"key":"1002_CR38","doi-asserted-by":"publisher","first-page":"2820","DOI":"10.3389\/fimmu.2019.02820","volume":"10","author":"S Gielis","year":"2019","unstructured":"Gielis, S. et al. Detection of enriched T cell epitope specificity in full T cell receptor sequence repertoires. Front. Immunol. 10, 2820 (2019).","journal-title":"Front. Immunol."},{"key":"1002_CR39","doi-asserted-by":"publisher","first-page":"82138","DOI":"10.1371\/journal.pone.0082138","volume":"8","author":"M Zhao","year":"2013","unstructured":"Zhao, M., Lee, W.-P., Garrison, E. P. & Marth, G. T. SSW library: an SIMD Smith-Waterman C\/C++ library for use in genomic applications. PLoS ONE 8, 82138 (2013).","journal-title":"PLoS ONE"},{"key":"1002_CR40","doi-asserted-by":"crossref","unstructured":"He, X. et al. Neural collaborative filtering. In Proc. 26th International Conference on World Wide Web 173\u2013182 (International World Wide Web Conferences Steering Committee, 2017).","DOI":"10.1145\/3038912.3052569"},{"key":"1002_CR41","doi-asserted-by":"publisher","unstructured":"Zhao, Y. et al. Dataset and model weights files introduced in the paper: a unified deep framework for peptide-major histocompatibility complex-T cell receptor binding prediction. Zenodo https:\/\/doi.org\/10.5281\/zenodo.14630611 (2025).","DOI":"10.5281\/zenodo.14630611"},{"key":"1002_CR42","doi-asserted-by":"publisher","unstructured":"Zhao, Y. et al. Source code for the paper: a unified deep framework for peptide-major histocompatibility complex-T cell receptor binding prediction. Zenodo https:\/\/doi.org\/10.5281\/zenodo.14625792 (2025).","DOI":"10.5281\/zenodo.14625792"}],"container-title":["Nature Machine Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s42256-025-01002-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42256-025-01002-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42256-025-01002-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T22:02:46Z","timestamp":1745359366000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s42256-025-01002-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,26]]},"references-count":42,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["1002"],"URL":"https:\/\/doi.org\/10.1038\/s42256-025-01002-0","relation":{},"ISSN":["2522-5839"],"issn-type":[{"value":"2522-5839","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,26]]},"assertion":[{"value":"8 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 February 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}