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., 2020).

1. Cavities described by 3D pharmacophoric points, generetaed with IChem Volsite (da Silva et al., 2018) or downloaded from the scPDB database. IChem is downloadable here,
2. A Linux/POSIX operating system,
3. Python with procare package and dependencies installed (see install).

ProCare install package consists of:

• A version of Open3D v. 0.5.0.0 (Zhou et al, 2018), modified to handle IChem VolSite chemical features,
• procare python scripts,
• procare launcher script procare_launcher.py.

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:

• scores file procare_scores.tsv (tab-separated) : simplified output
• procare.tsv : complete output containting transformation matrices elements
• using the --transform option will output rotated cavity mol2 (cfpfh_2rh1_cavity.mol2)
• using the --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.

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. 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 = {xx},
number = {xx},
pages = {xx},
year = {2020},
doi = {10.1021/acs.jmedchem.0c00422},
note ={PMID: 32496770},
URL = {https://doi.org/10.1021/acs.jmedchem.0c00422},
}

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

1. 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]

2. 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

3. 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.

4. 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

5. 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

6. Rusu, R. B.; Cousins, S. 3D Is Here: Point Cloud Library. 2012. https://doi.org/10.1109/ICRA.2011.5980567