{"ID":2860423,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.04377","arxiv_id":"2510.04377","title":"TCR-EML: Explainable Model Layers for TCR-pMHC Prediction","abstract":"T cell receptor (TCR) recognition of peptide-MHC (pMHC) complexes is a central component of adaptive immunity, with implications for vaccine design, cancer immunotherapy, and autoimmune disease. While recent advances in machine learning have improved prediction of TCR-pMHC binding, the most effective approaches are black-box transformer models that cannot provide a rationale for predictions. Post-hoc explanation methods can provide insight with respect to the input but do not explicitly model biochemical mechanisms (e.g. known binding regions), as in TCR-pMHC binding. ``Explain-by-design'' models (i.e., with architectural components that can be examined directly after training) have been explored in other domains, but have not been used for TCR-pMHC binding. We propose explainable model layers (TCR-EML) that can be incorporated into protein-language model backbones for TCR-pMHC modeling. Our approach uses prototype layers for amino acid residue contacts drawn from known TCR-pMHC binding mechanisms, enabling high-quality explanations for predicted TCR-pMHC binding. Experiments of our proposed method on large-scale datasets demonstrate competitive predictive accuracy and generalization, and evaluation on the TCR-XAI benchmark demonstrates improved explainability compared with existing approaches.","short_abstract":"T cell receptor (TCR) recognition of peptide-MHC (pMHC) complexes is a central component of adaptive immunity, with implications for vaccine design, cancer immunotherapy, and autoimmune disease. While recent advances in machine learning have improved prediction of TCR-pMHC binding, the most effective approaches are bla...","url_abs":"https://arxiv.org/abs/2510.04377","url_pdf":"https://arxiv.org/pdf/2510.04377v2","authors":"[\"Jiarui Li\",\"Zixiang Yin\",\"Zhengming Ding\",\"Samuel J. Landry\",\"Ramgopal R. Mettu\"]","published":"2025-10-05T21:47:48Z","proceeding":"q-bio.QM","tasks":"[\"q-bio.QM\",\"cs.CE\",\"cs.LG\"]","methods":"[\"Transformer\",\"Language Model\"]","has_code":false}