Structure-Regularized Interpretable TCR-Epitope Prediction
T cell receptor (TCR)-epitope binding prediction is essential for understanding adaptive immunity and developing immunotherapies. Existing sequence- and structure-based models often generalize poorly to unseen epitopes and provide limited interpretability. Furthermore, the impact of generated structures on model learning remains unclear. We present TCR-SRIM, a structure-regularized interpretable-by-design model that combines protein language model embeddings with interpretable contact prototypes to capture residue-level TCR-epitope interactions. TCR-SRIM achieves state-of-the-art predictive performance and improved interpretation quality on the TCR-XAI benchmark. Using its inherent interp...
| Authors | Jiarui Li, Zixiang Yin, Yunbei Zhang et al. |
|---|---|
| Primary category | q-bio.BM |
| Published | 29 June 2026 |
| Last updated at source | 29 June 2026 |
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