Data-driven Discovery of Biophysical T Cell Receptor Co-specificity Rules
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913906112331776 |
|---|---|
| author | Pyo, Andrew G. T. Nagano, Yuta Milighetti, Martina Henderson, James Callan Jr., Curtis G. Chain, Benny Wingreen, Ned S. Tiffeau-Mayer, Andreas |
| author_facet | Pyo, Andrew G. T. Nagano, Yuta Milighetti, Martina Henderson, James Callan Jr., Curtis G. Chain, Benny Wingreen, Ned S. Tiffeau-Mayer, Andreas |
| contents | The biophysical interactions between the T cell receptor (TCR) and its ligands determine the specificity of the cellular immune response. However, the immense diversity of receptors and ligands has made it challenging to discover generalizable rules across the distinct binding affinity landscapes created by different ligands. Here, we present an optimization framework for discovering biophysical rules that predict whether TCRs share specificity to a ligand. Applying this framework to TCRs associated with a collection of SARS-CoV-2 peptides we systematically characterize how co-specificity depends on the type and position of amino-acid differences between receptors. We also demonstrate that the inferred rules generalize to ligands highly dissimilar to any seen during training. Our analysis reveals that matching of steric properties between substituted amino acids is more important for receptor co-specificity than the hydrophobic properties that prominently determine evolutionary substitutability. Our analysis also quantifies the substantial importance of positions not in direct contact with the peptide for specificity. These findings highlight the potential for data-driven approaches to uncover the molecular mechanisms underpinning the specificity of adaptive immune responses. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_13722 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data-driven Discovery of Biophysical T Cell Receptor Co-specificity Rules Pyo, Andrew G. T. Nagano, Yuta Milighetti, Martina Henderson, James Callan Jr., Curtis G. Chain, Benny Wingreen, Ned S. Tiffeau-Mayer, Andreas Biomolecules Machine Learning Biological Physics The biophysical interactions between the T cell receptor (TCR) and its ligands determine the specificity of the cellular immune response. However, the immense diversity of receptors and ligands has made it challenging to discover generalizable rules across the distinct binding affinity landscapes created by different ligands. Here, we present an optimization framework for discovering biophysical rules that predict whether TCRs share specificity to a ligand. Applying this framework to TCRs associated with a collection of SARS-CoV-2 peptides we systematically characterize how co-specificity depends on the type and position of amino-acid differences between receptors. We also demonstrate that the inferred rules generalize to ligands highly dissimilar to any seen during training. Our analysis reveals that matching of steric properties between substituted amino acids is more important for receptor co-specificity than the hydrophobic properties that prominently determine evolutionary substitutability. Our analysis also quantifies the substantial importance of positions not in direct contact with the peptide for specificity. These findings highlight the potential for data-driven approaches to uncover the molecular mechanisms underpinning the specificity of adaptive immune responses. |
| title | Data-driven Discovery of Biophysical T Cell Receptor Co-specificity Rules |
| topic | Biomolecules Machine Learning Biological Physics |
| url | https://arxiv.org/abs/2412.13722 |