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CREMP

Generate conformer ensembles using CREST. Used to generate the CREMP database and the CREMP-CycPeptMPDB database described in CREMP: Conformer-rotamer ensembles of macrocyclic peptides for machine learning.

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Installation

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:

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. After downloading and extracting pickle.tar.gz, molecules can be loaded as follows:

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 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}
}