easydock 0.2.5

EasyDock Python module to facilitate molecular docking

EasyDock - Python module to automate molecular docking

Installation

pip install easydock

or the latest version from github

pip install git+https://github.com/ci-lab-cz/easydock.git

Dependencies

from conda

conda install -c conda-forge python=3.9 numpy=1.20 rdkit scipy dask distributed

from pypi

pip install meeko

to use multiple servers for docking install paramiko (backend of dask if launched from the command line)

pip install paramiko

Installation of vina. There is a strict recommendation to install vina from sources https://github.com/ccsb-scripps/AutoDock-Vina, because version 1.2.3 (available from pip) contains a bug which result in very unstable docking scores. The fix will be included in later releases.

Installation of gnina is described at https://github.com/gnina/gnina

Description

Fully automatic pipeline for molecular docking.

Features:

• the major script run_dock supports docking with vina and gnina (gnina also supports smina and its custom scoring functions)
• can be used as a command line utility or imported as a python module
• supports distributed computing using dask library
• supports docking of boron-containing compounds using vina and smina (boron is replaced with carbon before docking and returned back)
• all outputs are stored in an SQLite database
• interrupted calculations can be restarted by invoking the same command or by supplying just a single argument - the existing output database
• get_sdf_from_dock_db is used to extract data from output DB

Pipeline:

• input SMILES are converted in 3D by RDKit, 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 using Meeko
• docking with vina/gnina
• output poses are converted in MOL format and stored into output DB along with docking scores

Example

Docking from command line

Docking using vina takes input SMILES and a config file. Ligands will not be protonated with Chemaxon, so their supplied charged states will be used. 4 CPU cores will be used (4 molecules will be dock in parallel). When docking will finish an SDF file will be created with top docking poses for each ligand.

run_dock -i input.smi -o output.db --program vina --config config.yml --no_protonation -c 4 --sdf

Example of config.yml for vina docking

protein: /path/to/protein.pdbqt
protein_setup: /path/to/grid.txt
exhaustiveness: 8
seed: 0
n_poses: 5
ncpu: 5

NOTE: ncpu argument in run_dock and config.yml has different meaning. In run_dock it means the number of molecules docked in parallel. In config.yml it means the number of CPUs used for docking of a single molecule. The product of these two values should be equal or a little bit more than the number of CPUs on a computer.

The same but using gnina

run_dock -i input.smi -o output.db --program gnina --config config.yml --no_protonation -c 4 --sdf

Example of config.yml for gnina docking

script_file: /path/to/gnina_executable
protein: /path/to/protein.pdbqt
protein_setup: /path/to/grid.txt
exhaustiveness: 8
scoring: default
cnn_scoring: rescore
cnn: dense_ensemble
n_poses: 10
addH: False
ncpu: 1
seed: 0

To use smina invoke gnina as shown above and make corresponding changes in config.yml

script_file: /path/to/gnina_executable
protein: /path/to/protein.pdbqt
protein_setup: /path/to/grid.txt
exhaustiveness: 8
scoring: vinardo
cnn_scoring: None
cnn: dense_ensemble
n_poses: 10
addH: False
ncpu: 1
seed: 0

Docking using multiple servers

To distribute docking over multiple servers one have to start dask cluster and call the script

dask ssh --hostfile $PBS_NODEFILE --nworkers 15 --nthreads 1 &
sleep 10
run_dock -i input.smi -o output.db --program vina --config config.yml --no_protonation --sdf --hostfile $PBS_NODEFILE --dask_report

$PBS_NODEFILE is a file containing list of IP addresses of servers. The first one from the list will be used by a dask scheduler, but it will also participate in computations.

--nworkers is the number of workers per host. This is the number of molecules which are docked in parallel on a single host.

--nthreads can be any value. The number of CPUs used for docking of a single molecule will be taken from config.yml.

--dask_report argument will create at the end of calculations an html-file with performance report (may be useful to tweak docking parameters).

Important setup issue - the limit of open files on every server should be increased to the level at least twice the total number of requested workers (file streams are used for inter-node communication by dask).

Data retrieval from the output database

To extract data from the database one may use the script get_sdf_from_dock_db.

Extract top poses with their scores (additional information in DB fields can be extracted only for the top poses):

get_sdf_from_dock_db -i output.db -o output.sdf --fields docking_score

Retrieve second poses for compounds mol_1 and mol_4 in SDF format:

get_sdf_from_dock_db -i output.db -o output.sdf -d mol_1 mol_4 --poses 2 

Instead of a 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_dock_db -i output.db -o output.sdf --fields docking_score --add_sql 'docking_score < -10' 

Docking from Python

Dock a list of molecules on a local computer. Import mol_dock function from a corresponding submodule.

from easydock.run_dock import docking
from easydock.vina_dock import mol_dock
# from easydock.gnina_dock import mol_dock  # <- enable gnina docking
from rdkit import Chem

smiles = ['CC(=O)O', 'NCC(=O)O', 'NC(C)C(=O)O']
mols = [Chem.MolFromSmiles(smi) for smi in smiles]

# assign names, because this will be an identifier of docking outputs of a molecule 
for mol, smi in zip(mols, smiles):
    mol.SetProp('_Name', smi)

for mol_id, res in docking(mols, dock_func=mol_dock, dock_config='config.yml', ncpu=4):
    print(mol_id, res)

Customization

To implement support of a custom docking program one should implement a function like mol_dock which will take as input an RDKit mol object (named molecule) and an yml-file with all docking parameters. The function should run a command line script/utility and return back a tuple of a molecule name and a dictionary of parameters and their values which should be stored in DB (parameter names should be exactly the same as corresponding field names in DB). For examples, please look at mol_dock functions in vina_dock or gnina_dock.

Changelog

0.2.5

• fix input argument type
• update examples and citation

0.2.4

• close pool explicitly to solve issue with multiprocessing
• replace subprocess calls with run
• explicitly set types of command line arguments which a file paths (solve issue with relative paths)

0.2.3

• improve descriptions of examples on README
• catch all exceptions in conversion of PDBQT to Mol
• move DB related functions to a new database.py module
• use SMILES temporary file to protonate molecules with cxcalc
• add functions to get molecules from DB in Python (get_mols, select_from_db)

0.2.2

• fix bug with continuation of calculations after db was transferred to other machine
• restrict precedence of command line arguments over arguments restored from DB only to specific ones (output, hostfile, dask_report, ncpu, verbose)

0.2.1

• fix treatment of molecule ids in get_sdf_from_dock_db
• change installation instructions, vina must be installed from sources
• add argument no_tautomerization to disable tautomerization during protonation
• (critical) fix conversion of PDBQT to Mol which could not assign bond orders and returned molecules with only single bonds

0.2.0

• the stable version with multiple fixes and updates
• dask library was fully integrated and tested
• API was redesigned
• docking of boron-containing compounds was implemented (Vina, smina)
• a function to predict docking runtime was introduced

0.1.2

• (bugfix) docking of macrocycles is rigid (in future may be changed)

Licence

BSD-3

Citation

Minibaeva, G.; Ivanova, A.; Polishchuk, P.,
EasyDock: customizable and scalable docking tool.
Journal of Cheminformatics 2023, 15 (1), 102.
https://doi.org/10.1186/s13321-023-00772-2