tc-hard

This repository contains the code and the experiments for the paper On TCR Binding Predictors Failing to Generalize to Unseen Peptides published in Frontiers in Immunology. This work investigates TCR-peptide/-pMHC binding prediction on unseen peptides using state-of-the-art binding predictors. The notebooks used to create the TChard dataset are included in notebooks/notebooks.dataset/. The TChard dataset is available at: https://doi.org/10.5281/zenodo.6962043

Cite

If you find this repository useful, please cite our paper:

@article{graziolitcr,
  title={On TCR Binding Predictors Failing to Generalize to Unseen Peptides},
  author={Grazioli, Filippo and M{\"o}sch, Anja and Machart, Pierre and Li, Kai and Alqassem, Israa and O'Donnell, Timothy J and Min, Martin Renqiang},
  journal={Frontiers in Immunology},
  publisher={Frontiers},
  year={2022}
}

Content

tc-hard
│   README.md
│   ... 
│     
└───notebooks
│   │   notebooks.classification/ (TCR-peptide/-pMHC experiments)
│   │   notebooks.classification.results/ (plotting results of NetTCR2.0 and ERGO II)
│   │   notebooks.dataset/ (creation of the TChard dataset)   
│   
└───scripts/ (experiments, it mainly mirrors the content of notebooks.classification/)
│   
└───tcrmodels/ (Python package which wraps SOTA ML-based TCR models)

tcrmodels

tcrmodels wraps deep learning TCR prediction models. It includes:

• ERGO II
• NetTCR2.0

Install tcrmodels

cd tcrmodels
pip install .
pip install torch===1.4.0 torchvision===0.5.0 -f https://download.pytorch.org/whl/torch_stable.html

tcrmodels requires Python 3.6

References

ERGO II

Springer I, Tickotsky N and Louzoun Y (2021), Contribution of T Cell Receptor Alpha and Beta CDR3, MHC Typing, V and J Genes to Peptide Binding Prediction. Front. Immunol. 12:664514. DOI: https://doi.org/10.3389/fimmu.2021.664514

NetTCR-2.0

Montemurro, A., Schuster, V., Povlsen, H.R. et al. NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data. Commun Biol 4, 1060 (2021). DOI: https://doi.org/10.1038/s42003-021-02610-3

License

For the content of this repositoy, we provide a non-commercial license, see LICENSE.txt