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Description

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.

Requirements

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

Install (Linux/POSIX)

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,
• utils scripts

Easy install (no conda experience)

To easier installation, a bash script install.sh is provided.

1. Download the install package

$ git clone https://github.com/kimeguida/ProCare.git
$ cd ProCare/

2. Execute bash script install.sh

Will download miniconda and install procare.

$ bash install.sh <install_dir>

<install_dir> is the directory for installation. For example, $HOME.

3. Test installation

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.

For conda users

1. Download the install package

$ git clone https://github.com/kimeguida/ProCare.git
$ cd ProCare/

2. Create a python virtual environement

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.

3. Install procare python package

(procare) $ pip install procare_python_package/

4. Test installation

(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.

Usage

Comparison and alignment

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:

• procare_scores.tsv : (tab-separated) simplified score 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.

Visual inspection of superposed points

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:

• Matched points in the source and target cavities (aligned_2rh1_cavity.mol2, aligned_5d6l_cavity.mol2)
• procare_scores_contribution.tsv : proportion of pharmacophoric features in matched points of the source cavity

Help:

(procare) $ utils/procare_aligned_points.py --help

Scoring/rescoring superposed points

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:

• procare_rescoring.tsv : score file

Help:

(procare) $ utils/procare_rescoring.py --help

Apply a transformation to other mol2 objects in the source coordinates frame

(procare) $ python utils/procare_apply_transformation.py -f procare.tsv -a 2rh1_ligand.mol2 2rh1_cavity.mol2 -l 1

Outputs:

• Aligned objects (rot_2rh1_ligand.mol2 and rot_2rh1_cavity.mol2)

Help:

(procare) $ utils/procare_apply_transformation.py --help

Tip

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.

Citation

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},
}

Prospective applications

1. 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

2. 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.

3. CACHE hits prediction Challenge #1 https://cache-challenge.org

Code

https://github.com/kimeguida/ProCare
http://bioinfo-pharma.u-strasbg.fr/labwebsite/download.html

Signal issues

https://github.com/kimeguida/ProCare/issues

Support contacts

Merveille Eguida: keguida'[at]'unistra.fr
Didier Rognan, PhD: rognan'[at]'unistra.fr

References

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