https://github.com/genentech/cremp
CREMP: Conformer-Rotamer Ensembles of Macrocyclic Peptides for Machine Learning
https://github.com/genentech/cremp
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CREMP: Conformer-Rotamer Ensembles of Macrocyclic Peptides for Machine Learning
- Host: GitHub
- URL: https://github.com/genentech/cremp
- Owner: Genentech
- License: mit
- Created: 2024-02-20T20:26:20.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-08-13T18:05:28.000Z (almost 2 years ago)
- Last Synced: 2024-08-13T21:23:31.727Z (almost 2 years ago)
- Language: Python
- Homepage:
- Size: 371 KB
- Stars: 6
- Watchers: 4
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# CREMP
Generate conformer ensembles using CREST. Used to generate the [CREMP database](https://zenodo.org/doi/10.5281/zenodo.7931444) and the [CREMP-CycPeptMPDB database](https://zenodo.org/doi/10.5281/zenodo.10798261) described in [CREMP: Conformer-rotamer ensembles of macrocyclic peptides for machine learning](https://doi.org/10.1038/s41597-024-03698-y).
## Installation
```bash
conda env create -f env.yml
conda activate cremp
pip install -e .
```
To enable post-processing utilities, you need to install OpenEye and obtain a license:
```bash
conda install -c openeye openeye-toolkits
```
## Usage
Run the whole pipeline with `run_crest.py`. This embeds conformers with RDKit ETKDGv3, optimizes the low-energy ones with xTB, and subsequently runs CREST. Run `run_crest.py --help` for an overview of the options.
Alternatively, three subcommands are possible to run individual steps in the pipeline. These are
- `run_crest.py rdconf`
- `run_crest.py xtbopt`
- `run_crest.py crest`
To post-process the CREST output, use `scripts/postprocess.py`. See the instructions in the docstring of the `main` function for details about the expected file formats and directory structure.
## Downloading and using CREMP
The published CREMP dataset is available on [Zenodo](https://zenodo.org/doi/10.5281/zenodo.7931444). After downloading and extracting `pickle.tar.gz`, molecules can be loaded as follows:
```python
from cremp.utils.chem_utils import load_mol
mol_path = 'pickle/A.c.Men.S.pickle' # Replace with the path of your choice
mol_dict = load_mol(mol_path)
mol = mol_dict['rd_mol'] # Get RDKit molecule containing conformers
print(mol.GetNumConformers())
```
## License
Licensed under the MIT License. See [LICENSE](LICENSE) for additional details.
## Citation
If using the CREMP dataset, please cite
```
@article{grambow2024cremp,
title = {{CREMP: Conformer-rotamer ensembles of macrocyclic peptides for machine learning}},
author = {Grambow, Colin A. and Weir, Hayley and Cunningham, Christian N. and Biancalani, Tommaso and Chuang, Kangway V.},
year = {2024},
journal = {Scientific Data},
doi = {10.1038/s41597-024-03698-y},
pages = {859},
number = {1},
volume = {11}
}
```