polypharm 0.4.3

A Python-based library to perform IFD and MMGBSA calculations on different targets using a polypharmacological approach.

Polypharm

A Python-based library to perform induced fit docking (IFD) and MM/GBSA calculations on different targets using a polypharmacological approach.

Refer to the official documentation for details about installation, usage, methodology, and developer interface.

Installation

The version at the Python Package Index (PyPI) is always the latest stable release that is relatively bug-free and can be installed via pip:

pip install polypharm

The minimum Python version is 3.9, and requires the pandas and Jinja2 packages. Refer to the Installation page of the documentation for more details.

NOTE: The main functionality (i.e., IFD and MM/GBSA) does require a working Schrödinger Suite installation (2018-4 or greater) including the Glide and Prime modules.

Usage

polypharm can be used either programmatically or from the command line. There is Jupyter Notebook at the examples folder that shows the common usage. In any case, below is a brief example.

import glob
import os

import polypharm as ppm

# required to run docking and binding energy calculation
os.environ["SCHRODINGER_PATH"] = "/path/to/schrodinger"

# gather input files and configuration
prot_files = glob.glob(os.path.join("proteins", "*.mae*"))
lig_files = glob.glob(os.path.join("molecules", "*.mae*"))
resid_map = {
    "6lqa": ["B:1462", "B:1466", "B:1760", "B:1767"],
    "6rv3": [
        "A:126", "A:171", "A:194", "A:198", "A:199", "A:234", "A:235",
        "A:236", "A:238"
    ],
}
parallel = 10

# 1. Run induced-fit cross docking
ppm.cross_dock(prot_files, lig_files, resid_map, tasks=parallel)
# 2. Rescore generated IFD poses using MM/GBSA
ppm.rescore_docking(
    glob.glob(os.path.join("ifd", "**", "*", "*-out.maegz")),
    tasks=parallel,
)
# 3. Generate a report from MM/GBSA output
maefiles = glob.glob(os.path.join("mmgbsa", "**", "*-out.maegz"))
results = ppm.report(maefiles, resid_map, tasks=parallel)
# 4. Rank molecules by their docking performance
ranked_results = ppm.rank_molecules(results)

The same workflow can be performed via command line:

$ vim resids.txt # write residues into a file
$ mkdir ifd && cd ifd
$ python -m polypharm dock -p ../proteins -r ../resids.txt -t 5 ../ligands
$ cd ..
$ mkdir mmgbsa && cd mmgbsa
$ python -m polypharm rescore -t 5 ../ifd
$ cd ..
$ python -m polypharm report -o report.csv -c 6 -t 5 mmgbsa
$ python -m polypharm rank -o rank.csv report.csv

Please refer to the official documentation for more information.

Citing

If you use polypharm in your research, please consider citing the following article:

To be added

Contributors

• Mauricio Bedoya - creator, maintainer
• Francisco Adasme - maintainer

License

Licensed under the MIT license, see the separate LICENSE file.