Rational Multi-Modal Transformers for TCR-pMHC Prediction
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914050429943808 |
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| author | Li, Jiarui Yin, Zixiang Ding, Zhengming Landry, Samuel J. Mettu, Ramgopal R. |
| author_facet | Li, Jiarui Yin, Zixiang Ding, Zhengming Landry, Samuel J. Mettu, Ramgopal R. |
| contents | T cell receptor (TCR) recognition of peptide-MHC (pMHC) complexes is fundamental to adaptive immunity and central to the development of T cell-based immunotherapies. While transformer-based models have shown promise in predicting TCR-pMHC interactions, most lack a systematic and explainable approach to architecture design. We present an approach that uses a new post-hoc explainability method to inform the construction of a novel encoder-decoder transformer model. By identifying the most informative combinations of TCR and epitope sequence inputs, we optimize cross-attention strategies, incorporate auxiliary training objectives, and introduce a novel early-stopping criterion based on explanation quality. Our framework achieves state-of-the-art predictive performance while simultaneously improving explainability, robustness, and generalization. This work establishes a principled, explanation-driven strategy for modeling TCR-pMHC binding and offers mechanistic insights into sequence-level binding behavior through the lens of deep learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_17305 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Rational Multi-Modal Transformers for TCR-pMHC Prediction Li, Jiarui Yin, Zixiang Ding, Zhengming Landry, Samuel J. Mettu, Ramgopal R. Computational Engineering, Finance, and Science Quantitative Methods T cell receptor (TCR) recognition of peptide-MHC (pMHC) complexes is fundamental to adaptive immunity and central to the development of T cell-based immunotherapies. While transformer-based models have shown promise in predicting TCR-pMHC interactions, most lack a systematic and explainable approach to architecture design. We present an approach that uses a new post-hoc explainability method to inform the construction of a novel encoder-decoder transformer model. By identifying the most informative combinations of TCR and epitope sequence inputs, we optimize cross-attention strategies, incorporate auxiliary training objectives, and introduce a novel early-stopping criterion based on explanation quality. Our framework achieves state-of-the-art predictive performance while simultaneously improving explainability, robustness, and generalization. This work establishes a principled, explanation-driven strategy for modeling TCR-pMHC binding and offers mechanistic insights into sequence-level binding behavior through the lens of deep learning. |
| title | Rational Multi-Modal Transformers for TCR-pMHC Prediction |
| topic | Computational Engineering, Finance, and Science Quantitative Methods |
| url | https://arxiv.org/abs/2509.17305 |