Streamlined version of VirtualFlow combining both VFVS and VFLP
Please clone the repository using:
git clone [email protected]:VirtualFlow/VF-Unity.git
Please ensure that the following packages are installed:
run_vf_unity.py: Main file that initiates docking calculations.initiate_calc.py: File that initiates scoring, docking calculations.lig_process.py: Process provided ligand into 3D format compatible with docking program (used by )pose_prediction.py: File for running pose prediction on processed ligands (used by pose_prediction).scoring_functions.py: File for running scoring on already docked ligands (used by initiate_calc.py).config.txt: Config file concisting of user definable parameters for running calculation./ligands/: Directory created by VF-Unity which will contain all processed ligands in a ready-to-dock format./outputs/: Directory created by VF-Unity which will contain docked ligand files.
We will be running QuickVina on a processed protein located in the config directory (5wiu_test.pdbqt). We provide a config.txt file which contains all parmeters for this simple run.
Please edit this file based on your preferance:
# The choice of the docking method
# Possible choices:
# AutoDock-Koto, AutodockVina_1.2, AutodockZN, EquiBind, FRED
# FitDock, GalaxyDock3, LigandFit, LightDock, M-Dock
# MCDock, MM-GBSA, PLANTS, PSOVina, RLDock
# SEED, adfr, autodock_cpu, autodock_gpu, AutodockVina_1.1.2
# dock6, flexx, glide, gnina, gnina-scoring
# gold, gwovina, iGemDock, idock, ledock
# molegro, nnscore2, qvina, qvina-w, rDock
# rf-score, rosetta-ligand, smina, smina-scoring, vina
# vina_carb, vina_xb, GlideSP, GlideXP, GlideHTVS
# qvina_gpu, qvina_w_gpu, vina_gpu, vina_gpu_2.0
# Please note: different pose prediction/docking methods can be combined with scoring functions.
# For example: ’qvina+nnscore2’.
# For supported choices/combinations please see the VirtualFlow homepage.
program_choice=qvina+nnscore2
# The x,y&z coordinates of the center of the docking space. The binding space describes the location where a molecule
# is allowed to bind.
center_x=-17.820
center_y=16.140
center_z=-18.643
# The size (in Angstroms) of the docking space in the x,y&z directions.
size_x=20
size_y=20
size_z=20
# How many poses to search for, providing a limit to the maximum number of iterations that a docking program performs in
# search for good poses
# Large exhaustiveness settings lead to increased computational costs.
exhaustiveness=10
# Molecule (either a string in smiles, selfies or amino-acid sequence) to be used for docking
# If the is_selfies=True, or is_peptide=True, a conversion from selfies->smiles
# and aa-sequence->smiles is performed.
smi=C1=CC(=CC=C1CSCC2C(C(C(O2)N3C=NC4=C(N=CN=C43)N)O)O)Cl
is_selfies=False
is_peptide=False
# Location to the prepared receptor file
# The receptor needs to be in the correct format, supported by the user's selected docking program.
# Additionally, the file needs to be present in the config directory.
receptor=./config/5wiu_test.pdbqt
To execute the program, please run:
python3 run_vf_unity.py
We note:
- The processed ligands will be located within the newly created ligands directory.
- The docked output from running QuickVina will be located in the newly created outputs directory.
- The default behaviour is for the program (QuickVina) is to make use of all available CPUs.
- A summary csv file
docking_output.csvis created (for running QuickVina):Ligand File,Docking Values,Docking Pose 7.pdbqt,"-9.9,-9.8,-9.1,-9.0,-9.0,-8.8,-8.8,-8.7,-8.6",./outputs/pose_7.pdbqt 14.pdbqt,"-9.6,-9.1,-9.0,-8.8,-8.7,-8.6,-8.6,-8.4,-8.4",./outputs/pose_14.pdbqt 6.pdbqt,"-9.8,-9.7,-9.2,-8.9,-8.8,-8.8,-8.7,-8.7,-8.5",./outputs/pose_6.pdbqt 9.pdbqt,"-9.3,-9.3,-9.3,-9.1,-9.1,-9.0,-9.0,-8.9,-8.8",./outputs/pose_9.pdbqt 12.pdbqt,"-8.9,-8.9,-8.8,-8.5,-8.5,-8.5,-8.5,-8.1,-8.0",./outputs/pose_12.pdbqt 4.pdbqt,"-9.6,-9.3,-9.2,-9.2,-9.0,-9.0,-8.8,-8.7,-8.7",./outputs/pose_4.pdbqt 3.pdbqt,"-9.9,-9.2,-9.0,-8.9,-8.8,-8.7,-8.6,-8.5,-8.4",./outputs/pose_3.pdbqt 1.pdbqt,"-9.4,-9.4,-9.1,-9.0,-8.9,-8.8,-8.7,-8.7,-8.7",./outputs/pose_1.pdbqt 5.pdbqt,"-9.8,-9.5,-9.4,-9.3,-9.3,-9.2,-9.1,-9.1,-9.0",./outputs/pose_5.pdbqt 10.pdbqt,"-9.6,-9.5,-9.4,-9.3,-9.2,-9.2,-9.1,-8.9,-8.8",./outputs/pose_10.pdbqt 2.pdbqt,"-9.6,-9.3,-8.9,-8.9,-8.8,-8.8,-8.8,-8.6,-8.5",./outputs/pose_2.pdbqt 15.pdbqt,"-9.8,-9.3,-9.2,-9.2,-9.1,-9.0,-9.0,-8.8,-8.8",./outputs/pose_15.pdbqt 13.pdbqt,"-9.1,-9.1,-9.0,-8.8,-8.7,-8.7,-8.7,-8.7,-8.7",./outputs/pose_13.pdbqt 8.pdbqt,"-9.1,-9.1,-9.1,-8.8,-8.7,-8.7,-8.6,-8.6,-8.5",./outputs/pose_8.pdbqt 11.pdbqt,"-9.7,-9.5,-9.3,-9.2,-9.2,-9.1,-9.0,-8.9,-8.8",./outputs/pose_11.pdbqt 0.pdbqt,"-9.6,-9.3,-9.3,-9.0,-8.9,-8.9,-8.7,-8.7,-8.5",./outputs/pose_0.pdbqt - Sepperately, a summary csv file is created (
rescoring_output.csv) is created when running rescoring (nnscore2):Docked Ligand,Re-scored Value ./outputs/pose_11.pdbqt,Kd = 114.94 fM;Kd = 2.29 pM;Kd = 3.22 fM;Kd = 10.3 fM;Kd = 11.92 fM;Kd = 0.87 fM;Kd = 307.09 fM;Kd = 2.18 pM;Kd = 22.69 pM ./outputs/pose_10.pdbqt,Kd = 14.22 fM;Kd = 13.13 fM;Kd = 10.96 pM;Kd = 29.48 fM;Kd = 73.28 fM;Kd = 2.35 fM;Kd = 108.96 fM;Kd = 122.82 fM;Kd = 0.35 fM ./outputs/pose_9.pdbqt,Kd = 216.4 fM;Kd = 685.98 fM;Kd = 155.95 fM;Kd = 116.24 fM;Kd = 72.66 fM;Kd = 189.04 pM;Kd = 4.74 pM;Kd = 7.01 pM;Kd = 1.04 nM ./outputs/pose_5.pdbqt,Kd = 0.14 fM;Kd = 3.58 pM;Kd = 2.75 fM;Kd = 23.24 fM;Kd = 7.79 pM;Kd = 152.06 nM;Kd = 0.12 fM;Kd = 121.77 fM;Kd = 18.27 pM ./outputs/pose_12.pdbqt,Kd = 0.16 fM;Kd = 11.43 fM;Kd = 1.76 pM;Kd = 4.74 pM;Kd = 2.15 pM;Kd = 4.79 pM;Kd = 364.24 fM;Kd = 55.44 fM;Kd = 20.77 fM ./outputs/pose_7.pdbqt,Kd = 101.4 fM;Kd = 0.12 fM;Kd = 1.14 fM;Kd = 134.77 fM;Kd = 0.55 fM;Kd = 10.53 nM;Kd = 0.0 fM;Kd = 129.84 fM;Kd = 8.86 nM ./outputs/pose_13.pdbqt,Kd = 0.34 fM;Kd = 0.13 fM;Kd = 1.74 pM;Kd = 73.35 fM;Kd = 203.96 fM;Kd = 11.28 pM;Kd = 3.75 pM;Kd = 85.11 fM;Kd = 5.17 pM ./outputs/pose_0.pdbqt,Kd = 19.81 fM;Kd = 0.01 fM;Kd = 0.06 fM;Kd = 14.96 fM;Kd = 0.1 fM;Kd = 578.15 fM;Kd = 120.14 fM;Kd = 24.31 fM;Kd = 3.01 pM ./outputs/pose_4.pdbqt,Kd = 12.63 fM;Kd = 2.81 pM;Kd = 0.0 fM;Kd = 52.42 fM;Kd = 0.96 fM;Kd = 310.52 fM;Kd = 36.57 pM;Kd = 17.94 fM;Kd = 0.13 fM ./outputs/pose_6.pdbqt,Kd = 0.91 fM;Kd = 8.34 pM;Kd = 0.14 fM;Kd = 0.19 fM;Kd = 0.02 fM;Kd = 0.0 fM;Kd = 16.4 fM;Kd = 60.84 fM;Kd = 1.34 pM ./outputs/pose_8.pdbqt,Kd = 94.53 fM;Kd = 23.83 pM;Kd = 6.22 pM;Kd = 1.34 fM;Kd = 276.47 fM;Kd = 0.63 fM;Kd = 71.78 fM;Kd = 5.64 fM;Kd = 18.55 pM ./outputs/pose_1.pdbqt,Kd = 0.13 fM;Kd = 0.09 fM;Kd = 11.47 pM;Kd = 11.69 fM;Kd = 5.36 pM;Kd = 8.35 pM;Kd = 2.44 pM;Kd = 4.19 pM;Kd = 23.34 pM ./outputs/pose_3.pdbqt,Kd = 0.0 fM;Kd = 1.65 fM;Kd = 14.99 fM;Kd = 0.24 fM;Kd = 213.73 pM;Kd = 6.34 pM;Kd = 472.1 fM;Kd = 329.75 pM;Kd = 261.08 fM ./outputs/pose_15.pdbqt,Kd = 1.06 pM;Kd = 136.55 fM;Kd = 1.82 pM;Kd = 106.86 fM;Kd = 4.26 pM;Kd = 1.55 pM;Kd = 6.23 pM;Kd = 61.89 fM;Kd = 242.37 pM ./outputs/pose_2.pdbqt,Kd = 11.5 fM;Kd = 18.0 fM;Kd = 2.34 pM;Kd = 783.94 fM;Kd = 202.75 fM;Kd = 79.9 fM;Kd = 210.65 fM;Kd = 13.13 fM;Kd = 511.8 fM ./outputs/pose_14.pdbqt,Kd = 162.12 fM;Kd = 726.22 fM;Kd = 10.38 fM;Kd = 432.15 fM;Kd = 0.06 fM;Kd = 9.24 pM;Kd = 3.07 pM;Kd = 23.07 nM;Kd = 7.97 pM
from run_vf_unity import main
program_choice = 'qvina'
scoring_function = 'nnscore2'
center_x = -17.820
center_y = 16.140
center_z = -18.643
size_x = 20
size_y = 20
size_z = 20
exhaustiveness = 10
smi = 'C1=CC(=CC=C1CSCC2C(C(C(O2)N3C=NC4=C(N=CN=C43)N)O)O)Cl'
is_selfies = False
is_peptide = False
receptor = './config/5wiu_test.pdbqt'
pose_pred_out, re_scored_values = main(program_choice, scoring_function, center_x, center_y, center_z, size_x, size_y, size_z, exhaustiveness, smi, is_selfies, is_peptide, receptor)
We note:
- The processed ligands will be located within the newly created ligands directory.
- The docked output from running QuickVina will be located in the newly created outputs directory.
- The default behaviour is for the program (QuickVina) is to make use of all available CPUs.
- The output from running th QuickVina calculation will be stored in the dictionary pose_pred_out.
- The output from running th QuickVina NNScore2.0 will be stored in the dictionary re_scored_values.
- No output csv files are created in this case.
Please prepare a file concisting of a list of smiles. For example, molecules.txt:
Index,Smiles
0,CCCCCCCCCC
1,C1=CC(=CC=C1CSCC2C(C(C(O2)N3C=NC4=C(N=CN=C43)N)O)O)Cl
The molecules can be run simply using out python function call:
import os
from run_vf_unity import main
program_choice = 'qvina'
scoring_function = ''
center_x = -17.820
center_y = 16.140
center_z = -18.643
size_x = 20
size_y = 20
size_z = 20
exhaustiveness = 10
is_selfies = False
is_peptide = False
receptor = './config/5wiu_test.pdbqt'
with open('./molecules.smi', 'r') as f:
lines = f.readlines()
lines = lines[1: ]
for item in lines:
idx,smi = item.split(',')
pose_pred_out, re_scored_values = main(program_choice, scoring_function, center_x, center_y, center_z, size_x, size_y, size_z, exhaustiveness, smi, is_selfies, is_peptide, receptor)
os.system('rm -rf ligands')
os.system('cp -a outputs outputs_{}'.format(idx))
The corresponding index (column 1) of a molecule in the molecules.txt file will be used for storing the results in outputs_*index*
Please compile the code using instructions from: https://github.com/ccsb-scripps/AutoDock-GPU.
After successfull compilation, within the bin directory, an executable will be made (example name: autodock_gpu_1wi). Then, the code is ready to run.
We provide an example inside ./executables/vf_gpu_example.zip. Inside the directory, a prepared protein-ligand pair is provided and the code can be run
using: ./autodock_gpu_1wi --ffile 1stp_protein.maps.fld --lfile ./1stp_ligand.pdbqt
Please download the code using instructions from: https://github.com/HannesStark/EquiBind. Please create a conda enviroment per the instructions of the EquiBind repository. Copy paste all files inside the working directory of VF-Unity.
Please compile the code using the instruction provided in: https://rdock.sourceforge.net/installation/.
We installed rDock using anacond (with conda install -c bioconda rdock)
Please install AmberTools: https://ambermd.org/GetAmber.php#ambertools. We managed performed the download using conda.
Additionally, please note: the variable chimera_path should be updated to location of the Chimera on your system. Chimera can be downloaded: https://www.cgl.ucsf.edu/chimera/download.html.
Requirments
Please install the ADFR software suite: https://ccsb.scripps.edu/adfr/downloads/.
Please install meeko with: pip install meeko.
Instructions for running AutoDockZN can be found on their official website: https://autodock-vina.readthedocs.io/en/latest/docking_zinc.html.
Requirments Please install the OpenEye software suite: https://docs.eyesopen.com/toolkits/python/quickstart-python/install.html.
Requirments Please install AmberTools: https://ambermd.org/GetAmber.php#ambertools.
Please paste the executable from https://github.com/oddt/rfscorevs_binary in the executables directory.
A qvina_gpu executable (of name 'qvina_gpu') should be placed in directory: /executables Instructions for compilation are provided in https://github.com/DeltaGroupNJUPT/QuickVina2-GPU
A qvina_gpu executable (of name 'qvina_w_gpu') should be placed in directory: /executables Instructions for compilation are provided in https://github.com/DeltaGroupNJUPT/QVina-W-GPU
A qvina_gpu executable (of name 'vina_gpu') should be placed in directory: /executables Instructions for compilation are provided in https://github.com/DeltaGroupNJUPT/Vina-GPU
A qvina_gpu executable (of name 'vina_gpu_2.0') should be placed in directory: /executables Instructions for compilation are provided in https://github.com/DeltaGroupNJUPT/Vina-GPU-2.0
An adfr executable (of name adfr) should be placed in directory: /executables directory. An executable needs to be compiled based on a user’s system using instructions described in https://ccsb.scripps.edu/adfr/downloads/.
An HDock executable (of name hdock) should be placed in directory: /executables directory. An createpl executable (of name createpl) should be placed in directory: /executables directory.
Requirments Please install OpenEye: https://docs.eyesopen.com/toolkits/python/quickstart-python/install.html. Additionally, a valid OpenEye licence is required. Namely, a file named oe_license.txt needs to be placed in the working directory.
Please paste the executable from https://galaxy.seoklab.org/files/by2hsnvxjf/softwares/galaxydock.html in the executables directory. Note a data directory (name 'data') is required for successful runs.
We suggest downloading LightDock from: https://github.com/lightdock/lightdock. For this, within the current working directory (VF-Unity), please run:
git clone https://github.com/lightdock/lightdock.git
virtualenv venv
source venv/bin/activate
cd lightdock
pip install -e .
Please add the executable (of name MpSDock) in executables directory.
Please install AmberTools: https://ambermd.org/GetAmber.php#ambertools.
We managed performed the download using conda.
Please note: the variable chimera_path should be updated to location of the Chimera on your system. Chimera can be downloaded: https://www.cgl.ucsf.edu/chimera/download.html.
A working dock6 download (with a valid licence) is required. The variable dock6_path should be updated to location of the Chimera on your system.
A box.in input file is required for the program. Please specify the path in variable box_in_file.
A grid.in input file is required for the program. Please specify the path in variable grid_in_file.
A dock.in input file is required for the program. Please specify the path in variable dock_in_file.
A valid Schrödinger license is required to run CovDock.
A valid Schrödinger license is required to run GlideSP/XP/HTVS.
If you are interested in contributing to VirtualFlow, whether it is to report a bug or to extend VirtualFlow with your own code, please see the file CONTRIBUTING.md and the file CODE_OF_CONDUCT.md.
The project ist distributed under the GNU GPL v2.0. Please see the file LICENSE for more details.
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