{"id":13774345,"url":"https://github.com/tbepler/protein-sequence-embedding-iclr2019","last_synced_at":"2025-07-29T01:38:06.502Z","repository":{"id":44404567,"uuid":"172108299","full_name":"tbepler/protein-sequence-embedding-iclr2019","owner":"tbepler","description":"Source code for \"Learning protein sequence embeddings using information from structure\" - ICLR 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Learning"],"sub_categories":[],"readme":"# Learning protein sequence embeddings using information from structure\n\nNew and improved embedding models combining sequence and structure training are now available at https://github.com/tbepler/prose!\n\n\u003cbr /\u003e\n\u003cbr /\u003e\n\nThis repository contains the source code and links to the data and pretrained embedding models accompanying the ICLR 2019 paper: [Learning protein sequence embeddings using information from structure](https://openreview.net/pdf?id=SygLehCqtm)\n\n```\n@inproceedings{\nbepler2018learning,\ntitle={Learning protein sequence embeddings using information from structure},\nauthor={Tristan Bepler and Bonnie Berger},\nbooktitle={International Conference on Learning Representations},\nyear={2019},\n}\n```\n\n## Setup and dependencies \n\nDependencies:\n- python 3\n- pytorch \u003e= 0.4\n- numpy\n- scipy\n- pandas\n- sklearn\n- cython\n- h5py (for embedding script)\n\nRun setup.py to compile the cython files:\n\n```\npython setup.py build_ext --inplace\n```\n\n## Data sets\n\nThe data sets with train/dev/test splits are provided as .tar.gz files from the links below.\n\n- [SCOPe data](http://bergerlab-downloads.csail.mit.edu/bepler-protein-sequence-embeddings-from-structure-iclr2019/scope.tar.gz)\n- [Pfam data](http://bergerlab-downloads.csail.mit.edu/bepler-protein-sequence-embeddings-from-structure-iclr2019/pfam.tar.gz)\n- [Protein secondary structure data](http://bergerlab-downloads.csail.mit.edu/bepler-protein-sequence-embeddings-from-structure-iclr2019/secstr.tar.gz)\n- [Transmembrane data](http://bergerlab-downloads.csail.mit.edu/bepler-protein-sequence-embeddings-from-structure-iclr2019/transmembrane.tar.gz)\n- [CASP12 contact map data](http://bergerlab-downloads.csail.mit.edu/bepler-protein-sequence-embeddings-from-structure-iclr2019/casp12.tar.gz)\n\nThe training and evaluation scripts assume that these data sets have been extracted into a directory called 'data'.\n\n## Pretrained models\n\nOur trained versions of the structure-based embedding models and the bidirectional language model can be downloaded [here](http://bergerlab-downloads.csail.mit.edu/bepler-protein-sequence-embeddings-from-structure-iclr2019/pretrained_models.tar.gz).\n\n## Author\n\nTristan Bepler (tbepler@mit.edu)\n\n## Cite\n\nPlease cite the above paper if you use this code or pretrained models in your work.\n\n## License\n\nThe source code and trained models are provided free for non-commercial use under the terms of the CC BY-NC 4.0 license. See [LICENSE](LICENSE) file and/or https://creativecommons.org/licenses/by-nc/4.0/legalcode for more information.\n\n\n## Contact\n\nIf you have any questions, comments, or would like to report a bug, please file a Github issue or contact me at tbepler@mit.edu.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftbepler%2Fprotein-sequence-embedding-iclr2019","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftbepler%2Fprotein-sequence-embedding-iclr2019","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftbepler%2Fprotein-sequence-embedding-iclr2019/lists"}