{"ID":5439510,"CreatedAt":"2026-07-01T01:17:58.482524686Z","UpdatedAt":"2026-07-02T20:43:09.5019258Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.30902","arxiv_id":"2606.30902","title":"Structure-Regularized Interpretable TCR-Epitope Prediction","abstract":"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 interpretability, we further evaluate the effect of generated structures on model learning. While structures predicted by AlphaFold3, TCRModel2, and tFold-TCR yield competitive performance, they lead to less accurate interaction patterns and reduced binding-site diversity than experimentally-resolved structures. Our results highlight limitations of current structure prediction models for TCR-epitope learning and demonstrate the value of interpretable-by-design models for studying generated biological structures.","short_abstract":"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 learni...","url_abs":"https://arxiv.org/abs/2606.30902","url_pdf":"https://arxiv.org/pdf/2606.30902v1","authors":"[\"Jiarui Li\",\"Zixiang Yin\",\"Yunbei Zhang\",\"Janet Wang\",\"Samuel J. Landry\",\"Zhengming Ding\",\"Ramgopal R. Mettu\"]","published":"2026-06-29T20:48:35Z","proceeding":"q-bio.BM","tasks":"[\"q-bio.BM\",\"cs.CE\",\"cs.LG\"]","methods":"[\"Language Model\"]","has_code":false}