MetalHawk

This repository provides a Python implementation of the MetalHawk program to predict metal sites geometries.

M. Vrettas, PhD.

Installation

There are two options to install the software.

1. The easiest way is to visit the GitHub web-page of the project and download the code in zip format. This option does not require a prior installation of git on the computer.

2. Alternatively one can use git to clone the project directly as follows:

   $ git clone https://github.com/vrettasm/MetalHawk.git

Required packages

The recommended version is Python 3.8 (and above -- in that case you might see some warnings due to the newer python version used). Some required packages are:

scipy, numpy, pathlib, pandas, etc.

To ensure the required packages are installed just use:

$ pip install -r requirements.txt

Virtual environment (recommended)

It is highly advised to create a separate virtual environment to avoid messing with the main Python installation. On Linux and macOS systems type:

$ python3 -m venv metalhawk_venv

Note: "metalhawk_venv" is an optional name.

Once the virtual environment is created activate it with:

$ source metalhawk_venv/bin/activate

Make sure pip is updated:

$ python3 -m pip install --upgrade pip

Then we can install all the requirements as above:

$ pip install -r requirements.txt

or

$ python3 -m pip install -r requirements.txt

N.B. For Windows systems follow the equivalent instructions.

Data extraction (conversion)

The program as it is expecting sphere pdb/csd files as input. For a generic input file you need to pass the input first from the 'extract_metal_sites.py'. The input of the script is a directory containing the generic 'pdb' or 'cif' files, while the output is a new directory containing the sphere 'pdb' files that can be used directly as input for MetalHawk. This script requires "pymol-open-source=2.5.0" to run.

How to run

To execute the program (within the activated virtual environment), you can either navigate to the main directory of the project (i.e. where the metalhawk.py is located), or locate it through the command line and then run the following command:

$ ./metalhawk.py -f path/to/filename.pdb

Hint: If you want to run the program on multiple files (in the same directory) you can use the '*' wildcard as follows:

$ ./metalhawk.py -f path/to/*.pdb

This will run MetalHawk on all the files (in the directory) with the '.pdb' extension.

Finally, if you want the output to be saved in a csv file format, use the '-o path/to/save/' option.

  $ ./metalhawk.py -f path/to/*.pdb -o /path/to/save/

This will use the "path/to/save/" and create a file with the output of the prediction. The filename is generated automatically.

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To explore all the options MetalHawk has to offer, type:

$ ./metalhawk.py -h

You will see the following menu:

References

The original work is described in detail at:

Gianmattia Sgueglia, Michail D. Vrettas, Marco Chino, Alfonso De Simone and Angela Lombardi (2023). "MetalHawk: Enhanced Classification of Metal Coordination Geometries by Artificial Neural Networks". Journal of Chemical Information and Modeling, DOI: 10.1021/acs.jcim.3c00873.

The MetalHawk logo (GitHub image) is designed by Gianmattia Sgueglia.

Documentation

Here you can find the documentation of the code.

Contact

For any questions/comments (regarding this code) please contact me at: vrettasm@gmail.com