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https://github.com/tbepler/protein-sequence-embedding-iclr2019
Source code for "Learning protein sequence embeddings using information from structure" - ICLR 2019
https://github.com/tbepler/protein-sequence-embedding-iclr2019
deep-learning language-model protein-embedding protein-modeling protein-representation-learning protein-sequence protein-structure pytorch recurrent-neural-networks
Last synced: 23 days ago
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Source code for "Learning protein sequence embeddings using information from structure" - ICLR 2019
- Host: GitHub
- URL: https://github.com/tbepler/protein-sequence-embedding-iclr2019
- Owner: tbepler
- License: other
- Created: 2019-02-22T17:38:16.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2021-06-16T17:22:06.000Z (over 3 years ago)
- Last Synced: 2025-01-16T07:11:00.747Z (30 days ago)
- Topics: deep-learning, language-model, protein-embedding, protein-modeling, protein-representation-learning, protein-sequence, protein-structure, pytorch, recurrent-neural-networks
- Language: Python
- Size: 50.8 KB
- Stars: 259
- Watchers: 11
- Forks: 75
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Learning protein sequence embeddings using information from structure
New and improved embedding models combining sequence and structure training are now available at https://github.com/tbepler/prose!
This 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)
```
@inproceedings{
bepler2018learning,
title={Learning protein sequence embeddings using information from structure},
author={Tristan Bepler and Bonnie Berger},
booktitle={International Conference on Learning Representations},
year={2019},
}
```
## Setup and dependencies
Dependencies:
- python 3
- pytorch >= 0.4
- numpy
- scipy
- pandas
- sklearn
- cython
- h5py (for embedding script)
Run setup.py to compile the cython files:
```
python setup.py build_ext --inplace
```
## Data sets
The data sets with train/dev/test splits are provided as .tar.gz files from the links below.
- [SCOPe data](http://bergerlab-downloads.csail.mit.edu/bepler-protein-sequence-embeddings-from-structure-iclr2019/scope.tar.gz)
- [Pfam data](http://bergerlab-downloads.csail.mit.edu/bepler-protein-sequence-embeddings-from-structure-iclr2019/pfam.tar.gz)
- [Protein secondary structure data](http://bergerlab-downloads.csail.mit.edu/bepler-protein-sequence-embeddings-from-structure-iclr2019/secstr.tar.gz)
- [Transmembrane data](http://bergerlab-downloads.csail.mit.edu/bepler-protein-sequence-embeddings-from-structure-iclr2019/transmembrane.tar.gz)
- [CASP12 contact map data](http://bergerlab-downloads.csail.mit.edu/bepler-protein-sequence-embeddings-from-structure-iclr2019/casp12.tar.gz)
The training and evaluation scripts assume that these data sets have been extracted into a directory called 'data'.
## Pretrained models
Our 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).
## Author
Tristan Bepler ([email protected])
## Cite
Please cite the above paper if you use this code or pretrained models in your work.
## License
The 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.
## Contact
If you have any questions, comments, or would like to report a bug, please file a Github issue or contact me at [email protected].