arXiv:2606.30902v1 Announce Type: cross 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
Advances in protein language models and computational biology are making highly specific and interpretable prediction methods for biological interactions possible.
Improved TCR-epitope prediction is crucial for developing targeted immunotherapies, vaccines, and understanding autoimmune diseases, impacting global health and biotech sectors.
The ability to accurately and interpretably predict TCR-epitope binding shifts from broad generalizations to specific residue-level interactions, enhancing drug discovery precision.
- · Biopharmaceutical companies
- · Immunotherapy developers
- · Computational biology researchers
- · AI/ML in healthcare
- · Traditional drug discovery methods
- · Trial-and-error immunology research
More effective and personalized cancer treatments and autoimmune disease therapies become feasible.
Accelerated development of universal vaccines and diagnostic tools based on precise immune response prediction.
Potential for AI-driven platforms to design novel TCRs or epitopes from scratch, revolutionizing synthetic biology and immune engineering.
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Read at arXiv cs.LG