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

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:

• 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

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},
}