A Jupyter Notebook to aid Docking to MurD protein

This notebook implements a typical protocol for docking ligands to a target protein. It uses RDKit (http://www.rdkit.org) to generate a number of reasonable conformations for each ligand and then uses SMINA (https://sourceforge.net/projects/smina/) to do the docking. Two methods of docking are implemented, the first docks into a rigid receptor, the second sets the protein side-chains around the active site to be flexible. Bear in mind flexible docking will be much, much slower. In the optional final step the resulting docked poses are rescored using a random forest model described in https://www.nature.com/articles/srep46710.

import sys
from collections import defaultdict
import numpy as np
from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit.Chem.Draw import IPythonConsole
from rdkit.Chem import PandasTools
import pandas as pd
IPythonConsole.ipython_3d=True
%pylab inline

import py3Dmol

Populating the interactive namespace from numpy and matplotlib

File location of structures for docking and file format

First we need get the location of the input file of structures you want to dock, replace "asinexSelection.sdf" with your file. You may want to rename the output file for conformations, and the output file containing the docked structures.

The sdf file needs to have the name included in the first line of each molecule record.

AEM 10028511 MOE2019 2D

22 24 0 0 0 0 0 0 0 0999 V2000 7.2040 -6.7290 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 6.3790 -6.7290 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0</code>

# File locations
sdfFilePath = 'asinexSelectionexport.sdf' # The input file of structures to generate conformations from
ConfoutputFilePath = 'asinexSelectionForDocking.sdf' # Output file containing conformations for docking

inputMols = [x for x in Chem.SDMolSupplier(sdfFilePath,removeHs=False)]
# Assign atomic chirality based on the structures:
len(inputMols) # Check how many strucures

10

#Check that all molecules have a name
for i, mol in enumerate(inputMols):
    if mol is None:
        print('Warning: Failed to read molecule %s in %s' % (i, sdfFilePath))
    if not mol.GetProp('_Name'):

        print('Warning: No name for molecule %s in %s' % (i, sdfFilePath))

Conformation generation

We next generate conformations, this uses paralellisation code from http://www.rdkit.org/docs/Cookbook.html contributed by Andrew Dalke. We don't use all cores on a desktop machine or it might be unresponsive. If running on a cluster you should modify this.

import multiprocessing

# Download this from http://pypi.python.org/pypi/futures
from concurrent import futures

# conda install progressbar
import progressbar

#Find number cores available, leave two or system might be unresponsive
numcores = multiprocessing.cpu_count()
max_workers = numcores -2

#Knowledge based torsion generator http://pubs.acs.org/doi/abs/10.1021/acs.jcim.5b00654
# This function is called in the subprocess.
# The parameters (molecule and number of conformers) are passed via a Python

ps = AllChem.ETKDG()
ps.pruneRmsThresh=0.5
ps.numThreads=0
#Edit for number of confs desired eg n = 5
n=5
def generateconformations(m, n, name):
    m = Chem.AddHs(m)
    ids=AllChem.EmbedMultipleConfs(m, n, ps)
    for id in ids:
        AllChem.UFFOptimizeMolecule(m, confId=id)
    # EmbedMultipleConfs returns a Boost-wrapped type which
    # cannot be pickled. Convert it to a Python list, which can.
    return m, list(ids), name

smi_input_file, sdf_output_file = sys.argv[1:3]


writer = Chem.SDWriter(ConfoutputFilePath)
# suppl = [x for x in Chem.SDMolSupplier(sdfFilePath,removeHs=False)]
#suppl = Chem.SmilesMolSupplier(smi_input_file, titleLine=False)

# for mol in suppl:
#     print(mol.GetPropsAsDict(includePrivate=True).get('_Name'))

with futures.ProcessPoolExecutor(max_workers=max_workers) as executor:
    # Submit a set of asynchronous jobs
    jobs = []
    for mol in inputMols:
        if mol:
            name = mol.GetProp('_Name')
            job = executor.submit(generateconformations, mol, n, name)
            jobs.append(job)

    widgets = ["Generating conformations; ", progressbar.Percentage(), " ",
               progressbar.ETA(), " ", progressbar.Bar()]
    pbar = progressbar.ProgressBar(widgets=widgets, maxval=len(jobs))
    for job in pbar(futures.as_completed(jobs)):
        mol, ids, name = job.result()
        mol.SetProp('_Name', name)
        for id in ids:
            writer.write(mol, confId=id)
writer.close()

Generating conformations; 100% Time: 0:00:00 |################################|

ms = [x for x in Chem.SDMolSupplier(ConfoutputFilePath,removeHs=False)]
# Assign atomic chirality based on the structures:
for m in ms: Chem.AssignAtomChiralTagsFromStructure(m)
len(ms) # check how many conformations

43

Docking to Protein

After generating the conformations we can now do the docking. In this example we use smina which can be downloaded from https://sourceforge.net/projects/smina/ you will need to know where smina has been installed. The protein and ligand examples provided are taken from https://fragalysis.diamond.ac.uk/viewer/react/preview/target/MURD MURD-x0373.

Docking using smina
Need protein minus the ligand in pdb format,
the ligand extracted from binding site in pdb format,
Conformations to be docked as sdf from conformation generation above
DockedFilePath = 'All_Docked.sdf.gz' is the File for the Docked structures

ProteinForDocking = 'protein_minus_ligand.pdb'
LigandFromProtein = '373ligand_only.pdb'
DockedFilePath = 'All_Docked.sdf.gz'
FlexibleDockedFilePath = 'FlexDocked.sdf.gz'

!'/usr/local/bin/smina.osx' --cpu 10 --seed 0 --autobox_ligand '{LigandFromProtein}' -r '{ProteinForDocking}' -l '{ConfoutputFilePath}' -o '{DockedFilePath}'

   _______  _______ _________ _        _______ 
  (  ____ \(       )\__   __/( (    /|(  ___  )
  | (    \/| () () |   ) (   |  \  ( || (   ) |
  | (_____ | || || |   | |   |   \ | || (___) |
  (_____  )| |(_)| |   | |   | (\ \) ||  ___  |
        ) || |   | |   | |   | | \   || (   ) |
  /\____) || )   ( |___) (___| )  \  || )   ( |
  \_______)|/     \|\_______/|/    )_)|/     \|


smina is based off AutoDock Vina. Please cite appropriately.

Weights      Terms
-0.035579    gauss(o=0,_w=0.5,_c=8)
-0.005156    gauss(o=3,_w=2,_c=8)
0.840245     repulsion(o=0,_c=8)
-0.035069    hydrophobic(g=0.5,_b=1.5,_c=8)
-0.587439    non_dir_h_bond(g=-0.7,_b=0,_c=8)
1.923        num_tors_div

Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -6.6       0.000      0.000    
2       -6.4       1.691      6.549    
3       -5.2       2.064      6.893    
4       -5.1       3.766      6.709    
5       -5.0       3.613      6.298    
6       -4.8       3.740      6.411    
7       -4.6       4.676      8.225    
8       -4.6       5.892      8.673    
9       -4.5       3.176      5.524    
Refine time 7.423
Using random seed: 0

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|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -6.7       0.000      0.000    
2       -6.3       1.876      6.678    
3       -4.9       3.628      6.174    
4       -4.9       3.733      6.342    
5       -4.8       3.711      6.584    
6       -4.5       3.799      6.532    
7       -4.4       4.702      8.174    
8       -4.3       4.226      7.838    
9       -4.1       1.771      6.292    
Refine time 7.661
Using random seed: 0

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|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -5.5       0.000      0.000    
2       -5.5       3.965      6.740    
3       -5.2       3.556      6.398    
4       -5.2       1.770      6.106    
5       -5.2       4.479      7.492    
6       -5.1       3.709      6.573    
7       -4.9       3.940      6.593    
8       -4.8       4.903      8.323    
9       -4.7       4.174      9.040    
Refine time 7.611
Using random seed: 0

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|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -5.4       0.000      0.000    
2       -4.6       3.707      7.904    
3       -4.6       3.299      6.242    
4       -4.4       3.029      7.644    
5       -4.4       3.723      6.810    
6       -4.2       2.701      3.547    
7       -4.2       4.196      8.036    
8       -4.1       4.284      6.331    
9       -3.9       3.557      7.935    
Refine time 8.006
Using random seed: 0

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|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -5.3       0.000      0.000    
2       -4.9       1.904      3.218    
3       -4.4       3.904      5.046    
4       -4.3       3.449      4.725    
5       -4.2       5.612      8.383    
6       -4.1       4.318      5.982    
7       -4.1       3.204      6.450    
8       -3.9       3.179      6.959    
9       -3.9       5.434      8.021    
Refine time 8.963
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -5.2       0.000      0.000    
2       -4.8       2.639      6.521    
3       -4.6       1.809      2.894    
4       -4.3       2.701      4.702    
5       -4.3       2.804      4.904    
6       -4.2       3.069      5.465    
7       -4.0       3.480      5.641    
8       -4.0       4.415      8.249    
9       -4.0       4.247      6.132    
Refine time 8.820
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -5.1       0.000      0.000    
2       -4.3       3.937      4.927    
3       -4.3       3.455      4.618    
4       -4.2       4.211      6.015    
5       -4.0       4.521      5.983    
6       -3.9       5.542      8.312    
7       -3.9       3.130      6.166    
8       -3.8       3.191      6.780    
9       -3.5       4.205      5.721    
Refine time 8.783
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -5.1       0.000      0.000    
2       -4.3       3.902      5.022    
3       -4.2       4.219      5.998    
4       -4.1       4.430      5.675    
5       -4.1       2.654      3.833    
6       -4.0       1.885      3.109    
7       -3.9       5.564      8.342    
8       -3.9       3.222      6.896    
9       -3.9       3.142      6.161    
Refine time 9.297
Using random seed: 0

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|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -6.6       0.000      0.000    
2       -6.4       2.262      7.803    
3       -5.9       3.647      4.488    
4       -5.9       3.091      7.882    
5       -5.6       2.940      7.976    
6       -5.3       4.377      8.572    
7       -5.1       2.801      8.087    
8       -5.1       4.518      6.633    
9       -5.0       7.305      10.294   
Refine time 8.994
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -5.5       0.000      0.000    
2       -5.4       1.923      2.555    
3       -5.3       1.492      1.565    
4       -4.9       1.537      2.259    
5       -4.9       3.787      4.803    
6       -4.7       4.300      6.234    
7       -4.7       2.877      8.280    
8       -4.6       4.159      5.546    
9       -4.6       4.707      5.818    
Refine time 8.440
Using random seed: 0

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|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -5.6       0.000      0.000    
2       -5.6       1.408      1.814    
3       -5.5       0.044      1.083    
4       -5.5       2.205      2.994    
5       -5.5       2.177      2.807    
6       -5.0       4.199      6.010    
7       -4.8       2.593      3.914    
8       -4.7       4.627      5.658    
9       -4.7       4.181      5.584    
Refine time 8.045
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -5.8       0.000      0.000    
2       -5.5       2.150      3.452    
3       -5.3       4.201      8.009    
4       -5.3       2.249      3.595    
5       -4.9       3.191      4.088    
6       -4.8       1.074      1.280    
7       -4.7       5.563      6.404    
8       -4.5       2.441      3.553    
9       -4.5       3.472      6.748    
Refine time 8.813
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -5.5       0.000      0.000    
2       -5.3       1.458      2.016    
3       -5.3       2.002      2.718    
4       -4.9       1.647      2.567    
5       -4.9       2.086      3.031    
6       -4.8       4.019      5.463    
7       -4.8       2.472      3.756    
8       -4.7       2.506      3.984    
9       -4.6       4.144      5.995    
Refine time 9.316
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -4.0       0.000      0.000    
2       -3.7       2.331      4.470    
3       -3.7       2.490      4.267    
4       -3.6       2.224      5.213    
5       -3.4       2.288      5.585    
6       -3.3       2.925      4.924    
7       -3.3       2.217      4.151    
8       -3.3       2.772      4.609    
9       -3.3       3.480      5.495    
Refine time 10.502
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -5.1       0.000      0.000    
2       -4.7       3.888      6.146    
3       -4.4       3.805      7.865    
4       -4.3       3.856      7.945    
5       -4.1       5.802      8.541    
6       -4.1       4.510      8.611    
7       -4.0       4.410      6.761    
8       -3.9       3.877      5.808    
9       -3.8       4.090      7.813    
Refine time 10.866
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -3.9       0.000      0.000    
2       -3.8       2.012      5.782    
3       -3.7       2.442      5.258    
4       -3.6       2.804      4.802    
5       -3.6       2.335      4.407    
6       -3.5       3.883      5.980    
7       -3.4       2.337      5.880    
8       -3.4       2.097      5.783    
9       -3.3       2.753      4.622    
Refine time 10.721
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -3.9       0.000      0.000    
2       -3.7       3.478      6.514    
3       -3.6       1.881      5.596    
4       -3.5       3.624      7.243    
5       -3.3       5.842      8.953    
6       -3.2       1.218      2.258    
7       -3.1       2.803      6.151    
8       -3.0       3.761      7.265    
9       -3.0       1.693      2.835    
Refine time 11.194
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -3.9       0.000      0.000    
2       -3.8       2.433      4.703    
3       -3.7       2.099      5.214    
4       -3.5       3.085      5.900    
5       -3.5       3.197      4.969    
6       -3.5       2.215      5.389    
7       -3.4       2.376      4.058    
8       -3.4       2.470      4.285    
9       -3.3       2.243      4.548    
Refine time 11.177
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -4.8       0.000      0.000    
2       -4.8       0.096      2.825    
3       -4.5       5.037      8.390    
4       -4.4       4.278      6.491    
5       -4.4       4.527      7.232    
6       -4.3       3.878      6.665    
7       -4.2       3.935      6.812    
8       -4.1       4.658      7.202    
9       -4.0       6.043      9.424    
Refine time 13.318
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -5.5       0.000      0.000    
2       -5.4       0.099      2.948    
3       -4.2       3.680      6.965    
4       -4.0       3.791      7.178    
5       -3.9       5.718      7.478    
6       -3.9       4.339      7.655    
7       -3.9       4.728      6.965    
8       -3.9       6.445      8.935    
9       -3.9       6.369      7.408    
Refine time 14.264
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -4.8       0.000      0.000    
2       -4.7       3.826      6.535    
3       -4.6       3.832      6.610    
4       -4.5       4.368      6.895    
5       -4.5       4.473      7.248    
6       -4.3       4.095      6.250    
7       -4.2       5.528      8.424    
8       -4.2       5.509      8.377    
9       -4.1       3.854      6.149    
Refine time 13.555
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -5.0       0.000      0.000    
2       -4.9       0.399      2.807    
3       -4.8       3.834      6.947    
4       -4.6       3.773      7.379    
5       -4.3       3.684      6.460    
6       -3.9       3.556      6.813    
7       -3.7       4.060      7.817    
8       -3.7       4.289      8.313    
9       -3.3       4.349      7.594    
Refine time 14.435
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -5.3       0.000      0.000    
2       -5.0       2.021      3.148    
3       -4.8       1.431      1.862    
4       -4.8       3.316      4.530    
5       -4.7       1.860      2.884    
6       -4.6       2.737      4.031    
7       -4.5       1.948      2.547    
8       -4.1       3.735      6.157    
9       -4.0       3.349      5.121    
Refine time 2.640
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -5.9       0.000      0.000    
2       -5.8       2.538      7.374    
3       -5.7       2.830      7.585    
4       -5.7       1.955      2.747    
5       -5.6       2.190      3.060    
6       -5.2       3.447      8.188    
7       -5.1       1.458      7.546    
8       -5.1       2.641      4.252    
9       -4.8       2.932      5.756    
Refine time 16.686
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -6.2       0.000      0.000    
2       -6.1       2.342      3.158    
3       -6.0       2.784      4.461    
4       -5.9       2.237      8.958    
5       -5.9       2.102      8.979    
6       -5.7       2.973      8.601    
7       -5.4       3.626      8.572    
8       -5.4       2.278      2.786    
9       -5.3       1.378      2.287    
Refine time 15.911
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -6.1       0.000      0.000    
2       -5.8       1.890      2.161    
3       -5.8       2.582      3.757    
4       -5.6       2.349      8.772    
5       -5.4       3.179      8.866    
6       -5.3       3.129      8.301    
7       -5.1       1.215      2.177    
8       -5.0       3.947      8.235    
9       -4.9       2.979      8.857    
Refine time 18.045
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -6.7       0.000      0.000    
2       -6.4       1.929      2.166    
3       -6.1       2.612      3.310    
4       -6.0       3.095      4.498    
5       -6.0       2.352      8.926    
6       -5.8       2.419      3.690    
7       -5.6       3.461      8.996    
8       -5.6       4.207      6.145    
9       -5.4       2.986      9.026    
Refine time 16.864
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -6.5       0.000      0.000    
2       -6.3       2.260      9.007    
3       -6.0       2.010      8.877    
4       -5.4       2.636      9.309    
5       -5.3       2.775      9.131    
6       -5.3       2.249      2.909    
7       -5.2       3.493      9.264    
8       -5.2       1.208      2.035    
9       -5.2       3.028      4.741    
Refine time 16.226
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -6.4       0.000      0.000    
2       -4.2       1.346      2.092    
3       -4.1       2.875      5.018    
4       -3.7       2.289      3.732    
5       -3.6       4.313      6.113    
Refine time 11.858
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -6.4       0.000      0.000    
2       -5.7       0.853      1.697    
3       -5.0       2.074      2.759    
4       -4.4       5.210      7.187    
5       -4.1       4.804      6.798    
6       -3.9       8.163      9.391    
7       -3.8       8.047      9.344    
8       -3.7       3.877      5.947    
9       -3.6       7.624      8.890    
Refine time 11.817
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -6.8       0.000      0.000    
2       -6.5       1.645      1.984    
3       -5.2       3.516      7.885    
4       -4.7       2.297      2.809    
5       -4.5       3.515      7.783    
6       -4.3       4.411      6.442    
7       -4.1       3.623      7.777    
8       -4.0       3.549      7.870    
9       -4.0       7.143      8.364    
Refine time 12.829
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -5.3       0.000      0.000    
2       -5.1       2.145      2.485    
3       -4.7       5.468      6.890    
4       -4.5       3.190      5.280    
5       -4.4       3.862      5.724    
6       -4.0       3.690      5.439    
7       -3.6       4.824      6.870    
8       -3.6       5.174      7.539    
9       -3.4       5.748      7.555    
Refine time 11.488
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -6.8       0.000      0.000    
2       -6.2       1.407      2.096    
3       -5.8       0.919      1.550    
4       -4.8       3.586      8.107    
5       -4.5       3.176      7.297    
6       -4.2       3.263      5.653    
7       -4.0       8.095      9.449    
Refine time 11.948
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -5.6       0.000      0.000    
2       -5.3       3.436      7.643    
3       -5.2       3.142      7.597    
4       -5.0       3.614      8.092    
5       -4.7       4.293      7.210    
6       -4.6       3.402      7.598    
7       -4.6       4.817      7.360    
8       -4.5       2.843      7.303    
9       -4.5       4.464      8.411    
Refine time 12.134
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -6.0       0.000      0.000    
2       -5.6       2.847      7.464    
3       -5.6       3.379      7.947    
4       -5.4       1.687      2.743    
5       -4.9       4.924      7.305    
6       -4.8       4.097      8.664    
7       -4.6       3.247      7.733    
8       -4.4       2.870      7.333    
9       -4.2       6.987      8.341    
Refine time 11.037
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -5.9       0.000      0.000    
2       -5.8       3.356      7.974    
3       -5.6       3.444      7.782    
4       -5.3       3.180      7.673    
5       -5.1       1.839      2.329    
6       -5.1       3.874      7.900    
7       -5.0       3.792      8.250    
8       -5.0       4.176      7.200    
9       -4.6       3.088      7.109    
Refine time 10.830
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -5.9       0.000      0.000    
2       -5.9       1.873      2.456    
3       -5.9       2.736      7.260    
4       -5.8       2.875      4.898    
5       -5.6       1.514      2.173    
6       -5.5       1.306      1.928    
7       -5.4       2.903      7.145    
8       -5.2       2.662      6.754    
9       -4.8       4.631      6.788    
Refine time 11.063
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -5.8       0.000      0.000    
2       -5.7       3.193      7.879    
3       -5.6       2.008      2.519    
4       -5.4       1.817      2.477    
5       -5.1       2.540      4.830    
6       -4.9       3.336      7.243    
7       -4.9       4.218      6.669    
8       -4.7       3.703      5.466    
9       -4.7       4.511      6.265    
Refine time 10.760
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -7.5       0.000      0.000    
2       -7.4       3.069      8.903    
3       -7.3       1.460      2.155    
4       -7.3       3.196      9.082    
5       -7.3       1.264      1.755    
6       -6.8       3.609      9.044    
7       -6.3       2.979      8.871    
8       -5.6       2.063      2.826    
9       -5.6       4.362      8.302    
Refine time 13.736
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -7.3       0.000      0.000    
2       -7.2       3.087      8.602    
3       -6.8       3.333      4.691    
4       -6.7       3.353      4.444    
5       -6.5       3.247      4.993    
6       -6.4       3.517      5.365    
7       -6.4       2.708      8.566    
8       -6.4       2.299      3.012    
9       -6.2       3.778      8.728    
Refine time 15.729
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -7.1       0.000      0.000    
2       -7.0       1.462      1.829    
3       -6.8       4.202      5.849    
4       -6.8       2.874      8.696    
5       -6.6       3.292      4.423    
6       -6.3       3.568      8.710    
7       -6.3       3.063      3.589    
8       -6.1       3.797      8.361    
9       -5.7       3.835      5.329    
Refine time 15.496
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -7.2       0.000      0.000    
2       -7.1       1.337      2.098    
3       -6.9       2.851      8.675    
4       -6.6       2.696      8.442    
5       -6.5       3.291      4.634    
6       -6.5       3.684      8.290    
7       -6.4       1.369      1.904    
8       -6.4       1.745      2.343    
9       -6.2       3.608      8.574    
Refine time 13.342
Using random seed: 0

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
1       -7.5       0.000      0.000    
2       -7.4       1.950      3.279    
3       -7.3       1.431      2.036    
4       -7.0       3.596      8.083    
5       -6.6       3.353      8.189    
6       -6.6       2.766      8.477    
7       -6.5       3.140      7.997    
8       -6.3       3.157      8.683    
9       -6.3       3.005      7.988    
Refine time 13.318
Loop time 516.905

Flexible docking method, set all side chains within specified distance to flexdist_ligand to flexible This will take an order of magnitude longer. Currently disabled, to enable remove the #

#!'/usr/local/bin/smina.osx' --cpu 10 --seed 0 --autobox_ligand '{LigandFromProtein}'  --autobox_add 5 -r '{ProteinForDocking}' --flexres A:124,A:132,A:143,A:147,A:311,A:312,A:434,A:435 -l '{ConfoutputFilePath}' -o '{FlexibleDockedFilePath}'

Rescoring using Random Forest Model

Optional, Rescore using a random forest model described in https://www.nature.com/articles/srep46710

Download from https://github.com/oddt/rfscorevs You will need the path to the binary

Path to protein containing ligand in pdb format
protein_plus_373ligand from Diamond
File to store rescored results

TargetProtein = 'protein_plus_373ligand.pdb'
scoreResults = 'DockedRescored.csv'

!/usr/local/bin/rf-score-vs --receptor '{TargetProtein}' '{DockedFilePath}' -o csv -O '{scoreResults}' --field name --field RFScoreVS_v2

docked_df = PandasTools.LoadSDF(DockedFilePath,molColName='Molecule', removeHs=False)

docked_df.head(n=5)

	minimizedAffinity	ID	Molecule
0	-6.63499	ASN 13983556
1	-6.39234	ASN 13983556
2	-5.19878	ASN 13983556
3	-5.12981	ASN 13983556
4	-4.96488	ASN 13983556

scores_df = pd.read_csv(scoreResults)

#scores_df.head(n=5)

results_df = pd.concat([docked_df, scores_df], axis=1)

results_df.head(5)

	minimizedAffinity	ID	Molecule	name	RFScoreVS_v2
0	-6.63499	ASN 13983556		ASN 13983556	6.128915
1	-6.39234	ASN 13983556		ASN 13983556	6.115107
2	-5.19878	ASN 13983556		ASN 13983556	6.067220
3	-5.12981	ASN 13983556		ASN 13983556	6.128303
4	-4.96488	ASN 13983556		ASN 13983556	6.093961

Saving the results

Now combine rescored file with docked structure file and export to "Alldata.sdf.gz" this is a big file so export compressed

combinedResults = 'Alldata.sdf.gz'

PandasTools.WriteSDF(results_df, combinedResults, molColName="Molecule", idName="ID", properties=list(results_df.columns))

results_df.sort_values(["RFScoreVS_v2"], axis=0, ascending=False, inplace=True) #or sort by scoring function
results_df.head(5)

	minimizedAffinity	ID	Molecule	name	RFScoreVS_v2
188	-4.10178	ASN 10790639		ASN 10790639	6.274990
346	-7.22396	AEM 10028511		AEM 10028511	6.253807
270	-4.49270	ART 13967891		ART 13967891	6.253746
348	-6.68619	AEM 10028511		AEM 10028511	6.253265
338	-7.34026	AEM 10028511		AEM 10028511	6.243394

results_df.sort_values(["minimizedAffinity"], axis=0, ascending=False, inplace=True) #or sort by minimizedAffinity
results_df.head(5)

	minimizedAffinity	ID	Molecule	name	RFScoreVS_v2
336	-7.47479	AEM 10028511		AEM 10028511	6.205563
372	-7.46385	AEM 10028511		AEM 10028511	6.171796
337	-7.43328	AEM 10028511		AEM 10028511	6.175466
373	-7.36845	AEM 10028511		AEM 10028511	6.203558
338	-7.34026	AEM 10028511		AEM 10028511	6.243394

selectedPose = 'selectedpose.sdf'
selectedPoseH = 'selectedposeH.sdf'

Export the top scoring molecules, edit "head(1)" to the number you want to export

PandasTools.WriteSDF(results_df.head(5), selectedPose, molColName="Molecule", idName="ID", properties=list(results_df.columns))

selecteddocked_df = PandasTools.LoadSDF(selectedPose,molColName='Molecule', removeHs=False)
selecteddocked_df

	minimizedAffinity	name	RFScoreVS_v2	ID	Molecule
0	-7.47479	AEM 10028511	6.205563	AEM 10028511
1	-7.46385	AEM 10028511	6.171796	AEM 10028511
2	-7.43328	AEM 10028511	6.175466	AEM 10028511
3	-7.36845	AEM 10028511	6.203558	AEM 10028511
4	-7.34026	AEM 10028511	6.243394	AEM 10028511

!obabel  -isdf 'selectedpose.sdf'  -osdf   -h  -O 'selectedposeH.sdf'

5 molecules converted

mH = Chem.MolFromMolFile(selectedPoseH, removeHs=False) #View first structure, Hydrogens present
mH

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
jupyter labextension install jupyterlab_3dmol

mols = [m for m in Chem.SDMolSupplier(selectedPoseH)]

def DrawDocking(protein,ligand):
     complex_pl = Chem.MolToPDBBlock(protein)
     docked_pdb = Chem.MolToPDBBlock(ligand)
     viewer = py3Dmol.view(width=800,height=800)
     viewer.addModel(complex_pl,'pdb')
     viewer.addModel(docked_pdb)
     prot = {'resn': ["DMS", "UNL", "SO4", "LIG", "HOH", "Cl"], 'invert': 1}  #define prot as all except list
     viewer.setStyle(prot,{'cartoon': {'colorscheme':'ssPyMol'}}) # Colour by secondary structure
     Lig_373 = {'resn' : 'LIG'} #original ligand in pdb file
     MyLig = {'resn':'UNL'} #ligand to be added from docking
     viewer.addSurface(py3Dmol.VDW,{'opacity':0.7, 'color': 'white'}, prot)
     viewer.setStyle({'resi': '132'}, {'stick': {'colorscheme': 'blueCarbon'}})
     viewer.setStyle({'resi': '147'}, {'stick': {'colorscheme': 'blueCarbon'}})
     viewer.setStyle({'resi': '311'}, {'stick': {'colorscheme': 'blueCarbon'}})
     viewer.setStyle(Lig_373,{'stick':{'colorscheme': 'whiteCarbon','radius':.1}}) 
     viewer.setStyle(MyLig,{'stick':{'colorscheme' : 'greenCarbon'}})
     viewer.zoomTo(MyLig)
     return viewer  

receptor = Chem.MolFromPDBFile(TargetProtein)
DrawDocking(receptor,mols[4])

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
jupyter labextension install jupyterlab_3dmol

<py3Dmol.view at 0x7fb0abbb4cf8>