{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T11:10:42Z","timestamp":1784545842595,"version":"3.55.0"},"reference-count":19,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T00:00:00Z","timestamp":1670371200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"publisher","award":["313271"],"award-info":[{"award-number":["313271"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"publisher","award":["314445"],"award-info":[{"award-number":["314445"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>T cells use T cell receptors (TCRs) to recognize small parts of antigens, called epitopes, presented by major histocompatibility complexes. Once an epitope is recognized, an immune response is initiated and T cell activation and proliferation by clonal expansion begin. Clonal populations of T cells with identical TCRs can remain in the body for years, thus forming immunological memory and potentially mappable immunological signatures, which could have implications in clinical applications including infectious diseases, autoimmunity and tumor immunology.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We introduce TCRconv, a deep learning model for predicting recognition between TCRs and epitopes. TCRconv uses a deep protein language model and convolutions to extract contextualized motifs and provides state-of-the-art TCR-epitope prediction accuracy. Using TCR repertoires from COVID-19 patients, we demonstrate that TCRconv can provide insight into T cell dynamics and phenotypes during the disease.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>TCRconv is available at https:\/\/github.com\/emmijokinen\/tcrconv.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac788","type":"journal-article","created":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T17:34:26Z","timestamp":1670434466000},"source":"Crossref","is-referenced-by-count":30,"title":["TCRconv: predicting recognition between T cell receptors and epitopes using contextualized motifs"],"prefix":"10.1093","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0060-6868","authenticated-orcid":false,"given":"Emmi","family":"Jokinen","sequence":"first","affiliation":[{"name":"Department of Computer Science, Aalto University , Espoo 02150, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0909-9484","authenticated-orcid":false,"given":"Alexandru","family":"Dumitrescu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Aalto University , Espoo 02150, Finland"},{"name":"Helsinki Institute of Life Science, University of Helsinki , Helsinki 00014, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jani","family":"Huuhtanen","sequence":"additional","affiliation":[{"name":"Department of Clinical Chemistry and Hematology, Translational Immunology Research Program, University of Helsinki , Helsinki 00290, Finland"},{"name":"Hematology Research Unit Helsinki, Helsinki University Hospital Comprehensive Cancer Center , Helsinki 00290, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vladimir","family":"Gligorijevi\u0107","sequence":"additional","affiliation":[{"name":"Center for Computational Biology (CCB), Flatiron Institute, Simons Foundation , New York, NY 10010, USA"},{"name":"Prescient Design, Genentech , New York, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Satu","family":"Mustjoki","sequence":"additional","affiliation":[{"name":"Department of Clinical Chemistry and Hematology, Translational Immunology Research Program, University of Helsinki , Helsinki 00290, Finland"},{"name":"Hematology Research Unit Helsinki, Helsinki University Hospital Comprehensive Cancer Center , Helsinki 00290, Finland"},{"name":"iCAN Digital Precision Cancer Medicine Flagship , Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard","family":"Bonneau","sequence":"additional","affiliation":[{"name":"Center for Computational Biology (CCB), Flatiron Institute, Simons Foundation , New York, NY 10010, USA"},{"name":"Prescient Design, Genentech , New York, NY, USA"},{"name":"Center for Data Science, New York University , New York, NY 10011, USA"},{"name":"Department of Computer Science, New York University, Courant Institute of Mathematical Sciences , New York, NY 10012, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Markus","family":"Heinonen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Aalto University , Espoo 02150, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harri","family":"L\u00e4hdesm\u00e4ki","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Aalto University , Espoo 02150, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,12,7]]},"reference":[{"key":"2023010805401838100_btac788-B1","doi-asserted-by":"crossref","first-page":"D1057","DOI":"10.1093\/nar\/gkz874","article-title":"VDJdb in 2019: database extension, new analysis infrastructure and a T-cell receptor motif compendium","volume":"48","author":"Bagaev","year":"2020","journal-title":"Nucleic Acids Res"},{"key":"2023010805401838100_btac788-B2","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1038\/nature22383","article-title":"Quantifiable predictive features define epitope-specific T cell receptor repertoires","volume":"547","author":"Dash","year":"2017","journal-title":"Nature"},{"key":"2023010805401838100_btac788-B3","author":"Elnaggar","year":"2020"},{"key":"2023010805401838100_btac788-B4","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1038\/ng.3822","article-title":"Immunosequencing identifies signatures of cytomegalovirus exposure history and HLA-mediated effects on the T cell repertoire","volume":"49","author":"Emerson","year":"2017","journal-title":"Nat. Genet"},{"key":"2023010805401838100_btac788-B5","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1038\/nature22976","article-title":"Identifying specificity groups in the T cell receptor repertoire","volume":"547","author":"Glanville","year":"2017","journal-title":"Nature"},{"key":"2023010805401838100_btac788-B6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-021-23303-9","article-title":"Structure-based protein function prediction using graph convolutional networks","volume":"12","author":"Gligorijevi\u0107","year":"2021","journal-title":"Nat. Commun"},{"key":"2023010805401838100_btac788-B7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-79682-4","article-title":"Transformer neural network for protein-specific de novo drug generation as a machine translation problem","volume":"11","author":"Grechishnikova","year":"2021","journal-title":"Sci. Rep"},{"key":"2023010805401838100_btac788-B8","first-page":"876","author":"Izmailov","year":"2018"},{"key":"2023010805401838100_btac788-B9","doi-asserted-by":"crossref","first-page":"e1008814","DOI":"10.1371\/journal.pcbi.1008814","article-title":"Predicting recognition between T cell receptors and epitopes with TCRGP","volume":"17","author":"Jokinen","year":"2021","journal-title":"PLoS Comput. Biol"},{"key":"2023010805401838100_btac788-B10","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1038\/s41591-020-0901-9","article-title":"Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19","volume":"26","author":"Liao","year":"2020","journal-title":"Nat. Med"},{"key":"2023010805401838100_btac788-B11","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1038\/s41590-021-01122-w","article-title":"The T cell immune response against SARS-CoV-2","volume":"23","author":"Moss","year":"2022","journal-title":"Nat. Immunol"},{"key":"2023010805401838100_btac788-B12","first-page":"1","author":"Nambiar","year":"2020"},{"key":"2023010805401838100_btac788-B13","author":"Nolan","year":"2020"},{"key":"2023010805401838100_btac788-B14","doi-asserted-by":"crossref","first-page":"13139","DOI":"10.1073\/pnas.1409155111","article-title":"Diversity and clonal selection in the human T-cell repertoire","volume":"111","author":"Qi","year":"2014","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023010805401838100_btac788-B15","first-page":"1","article-title":"DeepTCR is a deep learning framework for revealing sequence concepts within T-cell repertoires","volume":"12","author":"Sidhom","year":"2021","journal-title":"Nat. Commun"},{"key":"2023010805401838100_btac788-B16","author":"Snyder","year":"2020"},{"key":"2023010805401838100_btac788-B17","doi-asserted-by":"crossref","first-page":"664514","DOI":"10.3389\/fimmu.2021.664514","article-title":"Contribution of T cell receptor alpha and beta CDR3, MHC typing, V and J genes to peptide binding prediction","volume":"12","author":"Springer","year":"2021","journal-title":"Front. Immunol"},{"key":"2023010805401838100_btac788-B18","doi-asserted-by":"crossref","first-page":"107281","DOI":"10.1016\/j.compbiolchem.2020.107281","article-title":"SETE: sequence-based ensemble learning approach for TCR epitope binding prediction","volume":"87","author":"Tong","year":"2020","journal-title":"Comput. Biol. Chem"},{"key":"2023010805401838100_btac788-B19","author":"Vig","year":"2020"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btac788\/48181889\/btac788.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/39\/1\/btac788\/48520736\/btac788.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/39\/1\/btac788\/48520736\/btac788.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,8]],"date-time":"2023-01-08T05:42:01Z","timestamp":1673156521000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btac788\/6881078"}},"subtitle":[],"editor":[{"given":"Inanc","family":"Birol","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2022,12,7]]},"references-count":19,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,1,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btac788","relation":{},"ISSN":["1367-4811"],"issn-type":[{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,1,1]]},"published":{"date-parts":[[2022,12,7]]},"article-number":"btac788"}}