Mitigating negative data bias to enhance TCR-epitope binding and residue interaction prediction.

Authors

Xue Mi, Jinghua Zhu, Zhu Dai, Yuheng Zhu, Bo Ding, Hao Lin, Yang Shen, Guochun Cao, Zhongdang Xiao

Published in

Briefings in bioinformatics. Volume 27. Issue 4. Jul 03, 2026.

Abstract

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.

PMID:
42555504
Bibliographic data and abstract were imported from PubMed on 06 Aug 2026.

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