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https://github.com/kWeissenow/EMBER2
Alignment-free structure prediction using protein language models
https://github.com/kWeissenow/EMBER2
Last synced: 15 days ago
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Alignment-free structure prediction using protein language models
- Host: GitHub
- URL: https://github.com/kWeissenow/EMBER2
- Owner: kWeissenow
- Created: 2021-07-27T18:34:00.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2022-11-02T22:46:17.000Z (over 1 year ago)
- Last Synced: 2024-03-02T15:38:09.287Z (4 months ago)
- Language: Python
- Size: 23 MB
- Stars: 22
- Watchers: 4
- Forks: 6
- Open Issues: 0
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Metadata Files:
- Readme: README.md
Lists
- awesome-protein-design-software - structure prediction using EMBER2 and trRosetta - lower resource but can't match Alphafold2 (General protein language models)
README
# EMBER2
Alignment-free structure prediction using protein language models
# Requirements
The prediction pipeline uses Python3 and requires the following modules:
* numpy
* matplotlib
* torch (1.9.0 recommended)
* transformers (4.6.0 recommended)
The adapted [trRosetta](https://github.com/gjoni/trRosetta) folding pipeline additionally requires pyRosetta to be installed.
# Installation
Clone the repository and install the dependencies listed above.
The ProtT5 protein language model will be downloaded automatically on first use.
# Usage
For a FASTA file containing one or more protein sequences and an output directory of your choice, run the pipeline via
`python predict.py -i -o `
The ProtT5 model will be downloaded on first use and stored by default in the directory 'ProtT5-XL-U50'. You can change this directory with the `--t5_model` parameter.
You can trade speed with prediction quality by modifying the cropping stride used during inference (default: 16) with the `--stride` parameter (see publication for details).
If you run out of GPU memory and/or want to compute predictions for long protein sequences, you might want to lower the default batch-size of 200 with the `--batch_size` parameter.
You can create a PDB structure from a predicted distogram using the adapted trRosetta folding scripts in the 'folding' directory:
`python trRosetta.py -m 0 -pd 0.05 output.pdb`
Please note that the FASTA file for the folding script should only contain a single sequence corresponding to the distogram.
It is recommended to create multiple decoys with different cutoffs (-pd [0.05, 0.5]) and modes (-m {0,1,2}). Please refer to [trRosetta](https://github.com/gjoni/trRosetta) for additional details on the folding pipeline.
# Predictions for human proteome (<3000)
Predictions for all human proteins smaller than 3000 residues are available at [EMBER2_human](https://github.com/kWeissenow/EMBER2_human).
# Authors
Konstantin Weißenow, Michael Heinzinger, Burkhard Rost
Technical University Munich
# References
Weissenow, K., Heinzinger, M., Rost, B.
Protein language model embeddings for fast, accurate, and alignment-free protein structure prediction.
Structure (2022) [link](https://pubmed.ncbi.nlm.nih.gov/35609601/)