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 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.