Structure-Regularized Interpretable TCR-Epitope Prediction

• URL: http://arxiv.org/abs/2606.30902v1
• Date: Mon, 29 Jun 2026 20:48:35 GMT
• Title: Structure-Regularized Interpretable TCR-Epitope Prediction
• Abstract summary: T cell receptor (TCR)-epitope binding prediction is essential for understanding adaptive immunity and developing immunotherapies.<n>Existing sequence- and structure-based models often generalize poorly to unseens and provide limited interpretability.<n>We present TCR-SRIM, a structure-regularized interpretable-by-design model that combines protein language model embeddings with interpretable contact prototypes.
• Score: 32.262305380940724
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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