Sequential TCR-pMHC data was obtained from:
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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). https://doi.org/10.1038/s42003-021-02610-3
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Vita R, Mahajan S, Overton JA, Dhanda SK, Martini S, Cantrell JR, Wheeler DK, Sette A, Peters B. The Immune Epitope Database (IEDB): 2018 update. Nucleic Acids Res. 2018 Oct 24. doi: 10.1093/nar/gky1006. PMID: 30357391; PMCID: PMC6324067. www.iedb.org
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Goncharov, M., Bagaev, D., Shcherbinin, D. et al. VDJdb in the pandemic era: a compendium of T cell receptors specific for SARS-CoV-2. Nat Methods 19, 1017–1019 (2022). https://doi.org/10.1038/s41592-022-01578-0
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Tickotsky N, Sagiv T, Prilusky J, Shifrut E, Friedman N (2017). McPAS-TCR: A manually-curated catalogue of pathology-associated T cell receptor sequences. Bioinformatics 33:2924-2929 doi
3D TCR-pMHC complexes generated with Philip Bradley (2023) Structure-based prediction of T cell receptor:peptide-MHC interactions eLife 12:e82813 https://doi.org/10.7554/eLife.82813
Adapted from: Sverrisson, F., Feydy, J., Correia, B. E., & Bronstein, M. M. (2020). Fast end-to-end learning on protein surfaces. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 15272-15281 link
This project requires these specific versions. The PyKeops library is the bottleneck here.
| Dependency | Version |
|---|---|
| GCC | 9.2.0 |
| CMAKE | 3.22.2 |
| CUDA | 11.7 |
| cuDNN | 7.6.x |
| Python | 3.8.16 |
| PyTorch | 1.13.1 |
| PyKeops | 2.1.1 |
| PyTorch Geometric | 2.2.0 |
setup:
$ pip install -e .
Jean-Guillaume Brasier (Harvard IACS) & Marinka Zitnik (HMS DBMI)