Meeko: preparation of small molecules for AutoDock

Meeko reads an RDKit molecule object and writes a PDBQT string (or file) for AutoDock-Vina and AutoDock-GPU. Additionally, it has tools for post-processing of docking results which are not yet fully developed. Meeko supports the following features:

• Docking with explicit water molecules attached to the ligand (paper)
• Sampling of macrocyclic conformations during docking (paper)
• Creation of RDKit molecules with docked coordinates from PDBQT or DLG files without loss of bond orders.

Meeko is developed by the Forli lab at the Center for Computational Structural Biology (CCSB) at Scripps Research.

Recent changes

The --pH option was removed since v0.3.0. See issue https://github.com/forlilab/Meeko/issues/11 for more info.

Dependencies

• Python (>=3.5)
• Numpy
• Scipy
• RDKit

Conda or Miniconda can install the dependencies:

conda install -c conda-forge numpy scipy rdkit

Installation (from PyPI)

$ pip install meeko

If using conda, pip installs the package in the active environment.

Installation (from source)

$ git clone https://github.com/forlilab/Meeko
$ cd Meeko
$ pip install .

Optionally include --editable. Changes in the original package location take effect immediately without the need to run pip install . again.

$ pip install --editable .

Usage notes

Meeko does not calculate 3D coordinates or assign protonation states. Input molecules must have explicit hydrogens.

Examples using the command line scripts

mk_prepare_ligand.py -i molecule.sdf -o molecule.pdbqt
mk_prepare_ligand.py -i multi_mol.sdf --multimol_outdir folder_for_pdbqt_files
mk_copy_coords.py vina_results.pdbqt -o vina_results.sdf
mk_copy_coords.py adgpu_results.dlg -o adgpu_results.sdf

Quick Python tutorial

1. flexible macrocycle with attached waters

from meeko import MoleculePreparation
from rdkit import Chem

input_molecule_file = 'example/BACE_macrocycle/BACE_4.mol2'
mol = Chem.MolFromMol2File(input_molecule_file)

preparator = MoleculePreparation(hydrate=True) # macrocycles flexible by default since v0.3.0
preparator.prepare(mol)
preparator.show_setup()

output_pdbqt_file = "test_macrocycle_hydrate.pdbqt"
preparator.write_pdbqt_file(output_pdbqt_file)

Alternatively, the preparator can be initialized from a dictionary, which is useful for saving and loading configuration files with json. The command line tool mk_prepare_ligand.py can read the json files.

import json
from meeko import MoleculePreparation

mk_config = {"hydrate": True}
print(json.dumps(mk_config), file=open('mk_config.json', 'w'))
with open('mk_config.json') as f:
    mk_config = json.load(f)
preparator = MoleculePreparation.from_config(mk_config)

2. RDKit molecule from docking results

Assuming that 'docked.dlg' was written by AutoDock-GPU and that Meeko prepared the input ligands.

from meeko import PDBQTMolecule

with open("docked.dlg") as f:
    dlg_string = f.read()
pdbqt_mol = PDBQTMolecule(dlg_string, is_dlg=True, skip_typing=True)

# alternatively, read the .dlg file directly
pdbqt_mol = PDBQTMolecule.from_file("docked.dlg", is_dlg=True, skip_typing=True)

for pose in pdbqt_mol:
    rdkit_mol = pose.export_rdkit_mol()

For Vina's output PDBQT files, omit is_dlg=True.

pdbqt_mol = PDBQTMolecule.from_file("docking_results_from_vina.pdbqt", skip_typing=True)