ProCare is a point cloud registration approach to align protein cavities decribed by an ensemble of 3D points. Each point is labelled with one of eight pharmacophoric features complementary to the one of the closest protein atom, or a dummy feature where appropriate (Desaphy et al., 2012). More information in Eguida & Rognan, 2020 and procare manual.
ProCare install package consists of:
To easier installation, a bash script install.sh is provided.
$ git clone https://github.com/kimeguida/ProCare.git
$ cd ProCare/
Will download miniconda and install procare.
$ bash install.sh <install_dir>
<install_dir>
is the directory for installation. For example, $HOME
.
Execute bash commands in activate.sh, generated during installation. Note the change of the bash prompt.
(procare) $
(procare) $ python -c "import procare"
(procare) $ python -c "from procare.open3d.open3d.geometry import read_point_cloud"
No error means the installation has been successful.
$ git clone https://github.com/kimeguida/ProCare.git
$ cd ProCare/
With Conda/Anaconda:
$ conda env create -n procare -f procare_environment.yml
$ conda activate procare
Note that you may need to source your conda beforehand source /xxx/etc/profile.d/conda.sh
.
(procare) $ pip install procare_python_package/
(procare) $ python -c "import procare"
(procare) $ python -c "from procare.open3d.open3d.geometry import read_point_cloud"
No error means the installation has been successful.
Alignement is performed with the python script procare_launcher.py:
(procare) $ cd tests/
(procare) $ python procare_launcher.py -s 2rh1_cavity.mol2 -t 5d6l_cavity.mol2 --transform --ligandtransform 2rh1_ligand.mol2
Outputs:
--transform
option will output rotated cavity mol2 (cfpfh_2rh1_cavity.mol2)--ligandtransform
option with a ligand file as argument will output aligned ligand mol2 (cfpfh_2rh1_ligand.mol2)Help:
(procare) $ python procare_launcher.py --help
Will list possible options.
For visualization, associated points in the source and target cavity can be outputted by procare_aligned_points.py:
(procare) $ python utils/procare_aligned_points.py -c1 cfpfh_2rh1_cavity.mol2 -c2 5d6l_cavity.mol2 -o1 aligned_2rh1_cavity.mol2 -o2 aligned_5d6l_cavity.mol2
Outputs:
Help:
(procare) $ utils/procare_aligned_points.py --help
Rescoring of previously superposed cavities using other scoring schemes with procare_rescoring.py
(procare) $ python utils/procare_rescoring.py -s cfpfh_2rh1_cavity.mol2 -t 5d6l_cavity.mol2 -d 2
Outputs:
Help:
(procare) $ utils/procare_rescoring.py --help
(procare) $ python utils/procare_apply_transformation.py -f procare.tsv -a 2rh1_ligand.mol2 2rh1_cavity.mol2 -l 1
Outputs:
Help:
(procare) $ utils/procare_apply_transformation.py --help
Before executing, you need to activate the procare conda environment with conda activate procare
(you may need to source your conda first).
If you followed the "Easy install" procedure, you just need to execute commands in the activate.sh script.
If successful, the bash prompt will turn into:
(procare) $
...ready for computation.
If you use ProCare, please cite:
Eguida, M., Rognan, D. A Computer Vision Approach to Align and Compare Protein Cavities: Application to Fragment-Based Drug Design. J. Med. Chem. 2020, 63, 7127–7142. https://doi.org/10.1021/acs.jmedchem.0c00422.
@article{doi:10.1021/acs.jmedchem.0c00422,
author = {Eguida, Merveille and Rognan, Didier},
title = {A Computer Vision Approach to Align and Compare Protein Cavities: Application to Fragment-Based Drug Design},
journal = {Journal of Medicinal Chemistry},
volume = {63},
number = {13},
pages = {7127-7142},
year = {2020},
doi = {10.1021/acs.jmedchem.0c00422},
note ={PMID: 32496770},
URL = {https://doi.org/10.1021/acs.jmedchem.0c00422},
}
Eguida, M.; Rognan, D. Unexpected Similarity between HIV-1 Reverse Transcriptase and Tumor Necrosis Factor Binding Sites Revealed by Computer Vision. J. Cheminform. 2021, 13, 1–13
Eguida, M.; Schmitt-Valencia, C.; Hibert, M.; Villa, P.; Rognan, D. Target-Focused Library Design by Pocket-Applied Computer Vision and Fragment Deep Generative Linking. J. Med. Chem. 2022, 65, 13771–13783.
CACHE hits prediction Challenge #1 https://cache-challenge.org
https://github.com/kimeguida/ProCare
http://bioinfo-pharma.u-strasbg.fr/labwebsite/download.html
https://github.com/kimeguida/ProCare/issues
Merveille Eguida: keguida'[at]'unistra.fr
Didier Rognan, PhD: rognan'[at]'unistra.fr
Zhou, Q.-Y.; Park, J.; Koltun, V. Open3D: A Modern Library for 3D Data Processing. 2018. https://doi.org/10.1007/s00104-009-1793-x]
Desaphy, J.; Azdimousa, K.; Kellenberger, E.; Rognan, D. Comparison and Druggability Prediction of Protein–Ligand Binding Sites from Pharmacophore-Annotated Cavity Shapes. J. Chem. Inf. Model. 2012, 52 (8), 2287–2299. https://doi.org/10.1021/ci300184x
Da Silva, F.; Desaphy, J.; Rognan, D. IChem: A Versatile Toolkit for Detecting, Comparing, and Predicting Protein–Ligand Interactions. ChemMedChem 2018, 13 (6), 507–510. https://doi.org/10.1002/cmdc.201700505.
Rusu, R. B.; Blodow, N.; Beetz, M. Fast Point Feature Histograms (FPFH) for 3D Registration. IEEE Int. Conf. Robot. Autom. 2009, 3212–3217. https://doi.org/10.1109/ROBOT.2009.5152473
Rusu, R. B. Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments, 2010, Vol. 24. https://doi.org/10.1007/s13218-010-0059-6
Rusu, R. B.; Cousins, S. 3D Is Here: Point Cloud Library. 2012. https://doi.org/10.1109/ICRA.2011.5980567