{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T01:38:49Z","timestamp":1790905129048,"version":"4.1.0"},"reference-count":47,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T00:00:00Z","timestamp":1785888000000},"content-version":"vor","delay-in-days":35,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82072078"],"award-info":[{"award-number":["82072078"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Yishan Plan of Jiangsu Cancer Hospital","award":["YSZD202401"],"award-info":[{"award-number":["YSZD202401"]}]},{"name":"Key Research & Development Program of Jiangsu Province","award":["BE2020777"],"award-info":[{"award-number":["BE2020777"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Accurate prediction of the binding specificity between T-cell receptors (TCRs) and epitopes, along with the elucidation of their molecular interaction mechanisms, is pivotal for advancing immunotherapy and vaccine development. In this study, we propose a negative dataset construction strategy based on region-directed random mutations as an effective complement to traditional negative sampling methods. This strategy preserves the conserved amino acid motifs encoded by the V and J gene segments of the CDR3$\\beta$ sequence while introducing key residue mutations within the central junctional region. By constructing hard negatives, this approach encourages the model to capture more discriminative TCR-epitope binding features. Based on this optimized dataset, we developed TranTCR, a computational framework comprising two models: TranTCR-bind, which focuses on global sequence-level binding probability prediction, and TranTCR-map, which leverages transfer learning to translate global binding knowledge into fine-grained characterizations of residue-level interactions, such as inter-residue distances and contact scores. Experimental results demonstrate that TranTCR-bind exhibits superior predictive performance and generalization robustness across various negative sampling protocols. Furthermore, TranTCR-map utilizes attention mechanisms to deeply resolve complex inter-amino acid associations, enabling the identification of latent binding patterns and the revelation of TCR cross-reactivity characteristics. This study provides an efficient computational tool for the high-throughput screening of TCR repertoires and the digital characterization of immune recognition mechanisms.<\/jats:p>","DOI":"10.1093\/bib\/bbag418","type":"journal-article","created":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T11:41:56Z","timestamp":1784202116000},"source":"Crossref","is-referenced-by-count":2,"title":["Mitigating negative data bias to enhance TCR\u2013epitope binding and residue interaction prediction"],"prefix":"10.1093","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-1041-6370","authenticated-orcid":false,"given":"Xue","family":"Mi","sequence":"first","affiliation":[{"name":"State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University , No. 2 Sipailou, Xuanwu District, Nanjing 210096 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