phinder

Under active development - do not use

Semi-automatic identification of pharmacophore features from unbound receptor structure using gnina fragment docking.

Installation

Currently installation should be done by cloning this GitHub repository as well as following the installation instructions for GNINA 1.0 and rd-kit.

To install simply do the following command:

git clone https://github.com/gnina/phinder.git

Once you have cloned the repository you can work with the phinder scripts to generate pharmacophore!

Usage

Currently, Phinder is meant to be run within a project directory of which it's structure is very important for easily working with various files generated from the program. The best way to run phinder is to do the following:

1. Create a project and copy Phinder into said project

mkdir projectname
cp -r Phinder projectname/Phinder

2. Go into said project

cd projectname

Docking

Docking is done using GNINA 1.0 where we have locked in a set of probes for the fragment docking. Those probes are as follows:

• Acetate
• Acetamide
• Benzene
• Guanidine
• Imidazole
• Isobutane
• Isopropanol
• Isopropylamine
• Water

Phinder clusters the docked fragments to generate six pharmacophore feature types:

• Hydrogen bond donor
• Hydrogen bond acceptor
• Aromatic
• Hydrophobic
• Positive Ion
• Negative Ion

Phinding

To phind pharmacophores use the Phinder.py file on your receptor of choice. Phinder will auto-generate the binding pocket from an input ligand. Alternatively, you can provide Cartesian coordinates to pocket. Phinder will dock the fragments and generate a file of all pharmacophore features called GeneratedPharma.json. To run phinder use the following command:

## From Phinder Home Directory using ligand to auto-generate a pocket. The output directory defaults to the current directory. 
python Phinder.py -r receptor -l ligand -w output_directory

## From Phinder Home Directory using Cartesion coordinates to generate a pocket. 
## You must provide the center X,Y, and Z coordinates as well as the size in each coordinate
python Phinder.py -r receptor -p="coords" --coords=[X-coordinate,Y-coordinate,Z-coordinate,X-size,Y-size,Z-size] -w output_directory

Machine Learning

Phinder includes the option to utilize an MLP classifier trained on the PDB-Bind refined set (http://www.pdbbind.org.cn/index.php). If this optin is selected, Phinder will automatically output a json of the top 10 features ranked by this model called Top10.json.

python Phinder.py -r receptor -l ligand -w output_directory -m True 

Exhaustiveness

Gnina performs docking through Monte Carlo sampling. The maximum number of retained conformations per fragment defaults to 100, but can be set by the user.

#n=25 means a maximum of 25 poses per fragment will be retained
python Phinder.py -r receptor -l ligand -w output_directory -n 25 

Congrats! You have completed your first Phind! Now you can review your Pharmacophores in PyMOL. To do so make sure you have the load_query module for PyMOL it can be found here