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Code for "Attentive Variational Information Bottleneck for TCR-peptide Interaction Prediction", Grazioli et al., Bioinformatics 2022

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vibtcr

vibtcr is a Python package which implements the Mutlimodal Variational Information Bottleneck (MVIB) and the Attentive Variational Information Bottleneck (AVIB), with a focus on TCR-peptide interaction prediction.

Paper: "Attentive Variational Information Bottleneck for TCR–peptide interaction prediction", F. Grazioli, P. Machart, A. Mösch, K. Li, L.V. Castorina, N. Pfeifer, M.R. Min, Bioinformatics 2022

architecture

Install vibtcr

cd vibtcr
pip install .

Remark: vibtcr requires a different version of PyTorch than tcrmodels. It's recommended to install them in different environments.

Content

vibtcr
│   README.md
│   ... 
│   
└───data/
│   │   alpha-beta-splits/ (all TCR data, split in two disjoint sets: alpha+beta, beta-only)
│   │   ergo2-paper/ (data used in the ERGO II paper - contains VDJDB and McPAS)
│   │   mhc/ (the NetMHCIIpan-4.0 data)
│   │   nettcr2-paper/ (data used in the NetTCR2.0 paper - contains IEDB, VDJDB and MIRA)
│   │   vdjdb/ (complete VDJdb data from 5th of September)
│   
└───notebooks/
│   │   notebooks.classification/ (TCR-peptide experiments with AVIB/MVIB)
|   │   notebooks.mouse/ (identifying mouse TCRs as suitable OOD dataset)
│   │   notebooks.ood/ (out-of-distribution detection experiments with AVIB)
│   │   notebooks.regression/ (peptide-MHC BA regression with AVIB)
│   
└───tcrmodels/ (Python package which wraps SOTA ML-based TCR models)
│   
└───vibtcr/ (Python package which implements MVIB and AVIB for TCR-peptide interaction prediction)

tcrmodels

tcrmodels wraps state-of-the-art ML-based TCR prediction models. So far, it includes:

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

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 vibtcr, we provide a non-commercial license, see LICENSE.txt

Cite

If you find this work useful, please cite:

@article{10.1093/bioinformatics/btac820,
    author = {Grazioli, Filippo and Machart, Pierre and Mösch, Anja and Li, Kai and Castorina, Leonardo V and Pfeifer, Nico and Min, Martin Renqiang},
    title = "{Attentive Variational Information Bottleneck for TCR–peptide interaction prediction}",
    journal = {Bioinformatics},
    volume = {39},
    number = {1},
    year = {2022},
    month = {12},
    issn = {1367-4811},
    doi = {10.1093/bioinformatics/btac820},
    url = {https://doi.org/10.1093/bioinformatics/btac820},
    note = {btac820},
    eprint = {https://academic.oup.com/bioinformatics/article-pdf/39/1/btac820/48493569/btac820.pdf},
}

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Code for "Attentive Variational Information Bottleneck for TCR-peptide Interaction Prediction", Grazioli et al., Bioinformatics 2022

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