Python moldock to facilitate molecular docking
Project description
Python scripts to automate molecular docking
Installation
pip install moldock
or the latest version
pip install git+https://github.com/ci-lab-cz/docking-scripts.git
Dependencies
from conda
conda install -c conda-forge python=3.9 numpy=1.20 rdkit scipy dask distributed vina
from pypi
pip install meeko
Installation of gnina is described at https://github.com/gnina/gnina
Description
Fully automatic pipeline for molecular docking.
- two major scripts
vina_dock
andgnina_dock
which support docking withvina
andgnina
(gnina
also supportssmina
and its custom scoring functions) - can be used as command line scripts or imported as a python module
- support distributed computing using
dask
library - if calculation was interrupted it can be continued by invoking the same command, but everything except output DB may be omitted (these will be ignored nevertheless if DB exists) because all data is stored in DB at the first call
get_sdf_from_db
is used to extract data from output DB
Pipeline:
- input SMILES are converted in 3D by RDKit embedding, if input is 3D structures in SDF their conformations wil be taken as starting without changes.
- ligands are protonated by chemaxon at pH 7.4 and the most stable tautomers are generated (optional, requires a Chemaxon license)
- molecules are converted in PDBQT format
- docking with
vina
/gnina
- output poses are converted in MOL format and stored into output DB along with docking scores
Example
Both scripts vina_dock
and gnina_dock
have similar common arguments.
Docking using input SMILES, prepared protein and config files. Ligands will not be protonated with Chemaxon, so their supplied charged states will be used. 4 CPU cores will be used. When docking will finish an SDF will be created with top docking poses for each ligand.
vina_dock -i input.smi -o output.db -p protein.pdbqt -s vina_config --no_protonation -c 4 --sdf
Retrieve second poses for compounds mol_id_1
and mol_id_2
with their docking scores in SDF format:
get_sdf_from_db -i output.db -o out.sdf -d mol_id_1,mol_id_4 --fields docking_score --poses 2
Instead of a comma-separated list of ids a text file can be supplied as an argument -d
.
Retrieve top poses for compounds with docking score less then -10:
get_sdf_from_db -i output.db -o out.sdf --fields docking_score --poses 1 --add_sql 'docking_score < -10'
Use as a Python module
from moldock import vina_dock
vina_dock.iter_docking(dbname='output.db', receptor_pdbqt_fname='protein.pdbqt', protein_setup='vina_config.txt', protonation=False, exhaustiveness=8, seed=-1, n_poses=10, ncpu=4)
Changelog
0.1.2
- (bugfix) docking of macrocycles is rigid (in future may be changed)
Licence
CC BY-NC-SA 4.0
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file moldock-0.1.3.tar.gz
.
File metadata
- Download URL: moldock-0.1.3.tar.gz
- Upload date:
- Size: 17.0 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.23.0 setuptools/47.1.0.post20200528 requests-toolbelt/0.9.1 tqdm/4.47.0 CPython/3.6.9
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 42c525e85fa97cdb824cfbed3827819c6ae43a97ad15fcf60ac67abe12062458 |
|
MD5 | fe03e98f9708acdca8753fac2e039b68 |
|
BLAKE2b-256 | efc26878db411975255ab4415b8340a560c74c4c3454a3f34f3236632f29fda3 |
File details
Details for the file moldock-0.1.3-py3-none-any.whl
.
File metadata
- Download URL: moldock-0.1.3-py3-none-any.whl
- Upload date:
- Size: 22.1 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.23.0 setuptools/47.1.0.post20200528 requests-toolbelt/0.9.1 tqdm/4.47.0 CPython/3.6.9
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | f7fb9c89ba834dafe8a55476186726480d63728d223474c5e15e2e3f9f9d9fc5 |
|
MD5 | cc989074f35f125e37275a414e75ae6a |
|
BLAKE2b-256 | 5f556247f188516da1c086ed6dda72f99fc9af72ec75febf8ca087e4a487b910 |