Protein-ligand Docking tutorial using BioExcel Building Blocks (biobb)

PDB Cluster90 Binding Site Version


This tutorial aims to illustrate the process of protein-ligand docking, step by step, using the BioExcel Building Blocks library (biobb). The particular example used is the Mitogen-activated protein kinase 14 (p38-α) protein (PDB code 3HEC), a well-known Protein Kinase enzyme, in complex with the FDA-approved Imatinib, (PDB Ligand code STI, DrugBank Ligand Code DB00619), a small molecule kinase inhibitor used to treat certain types of cancer.

The tutorial will guide you through the process of identifying the active site cavity (pocket) without previous knowledge, and the final prediction of the protein-ligand complex.

Please note that docking algorithms, and in particular, AutoDock Vina program used in this tutorial, are non-deterministic. That means that results obtained when running the workflow could be diferent from the ones we obtained during the writing of this tutorial (see AutoDock Vina manual). We invite you to try the docking process several times to verify this behaviour.


Important: it is recommended to execute this tutorial step by step (not as a single workflow execution, Run All mode), as it has interactive selections.

Settings

Biobb modules used

  • biobb_io: Tools to fetch biomolecular data from public databases.

  • biobb_structure_utils: Tools to modify or extract information from a PDB structure file.

  • biobb_chemistry: Tools to perform chemoinformatics processes.

  • biobb_vs: Tools to perform virtual screening studies.

Auxiliar libraries used

  • nb_conda_kernels: Enables a Jupyter Notebook or JupyterLab application in one conda environment to access kernels for Python, R, and other languages found in other environments.

  • nglview: Jupyter/IPython widget to interactively view molecular structures and trajectories in notebooks.

  • ipywidgets: Interactive HTML widgets for Jupyter notebooks and the IPython kernel.

Conda Installation and Launch

git clone https://github.com/bioexcel/biobb_wf_virtual-screening.git
cd biobb_wf_virtual_screening
conda env create -f conda_env/environment.yml
conda activate biobb_VS_tutorial
jupyter-nbextension enable --py --user widgetsnbextension
jupyter-nbextension enable --py --user nglview
jupyter-notebook biobb_wf_virtual_screening/notebooks/clusterBindingSite/wf_vs_clusterBindingSite.ipynb

Input parameters

Input parameters needed:

  • pdb_code: PDB code of the experimental complex structure (if exists).
    In this particular example, the p38α structure in complex with the Imatinib drug was experimentally solved and deposited in the PDB database under the 3HEC PDB code. The protein structure from this PDB file will be used as a target protein for the docking process, after stripping the small molecule. An APO structure, or any other structure from the p38α cluster 100 (sharing a 100% of sequence similarity with the p38α structure) could also be used as a target protein. This structure of the protein-ligand complex will be also used in the last step of the tutorial to check how close the resulting docking pose is from the known experimental structure.


  • ligandCode: Ligand PDB code (3-letter code) for the small molecule (e.g. STI).
    In this particular example, the small molecule chosen for the tutorial is the FDA-approved drug Imatinib (PDB Code STI), a type of cancer growth blocker, used in diferent types of leukemia.

import nglview
import ipywidgets

pdb_code = "3HEC"         # P38 + Imatinib

ligand_code = "STI"       # Imatinib


Fetching PDB structure

Downloading PDB structure with the protein molecule from the PDBe database.
Alternatively, a PDB file can be used as starting structure.


Building Blocks used:

  • Pdb from biobb_io.api.pdb


from biobb_io.api.pdb import pdb

download_pdb = "download.pdb"
prop = {
  "pdb_code": pdb_code,
  "filter": ["ATOM", "HETATM"]
}

pdb(output_pdb_path=download_pdb,
    properties=prop)

Visualizing 3D structure

Visualizing the downloaded/given PDB structure using NGL.

Note (and try to identify) the Imatinib small molecule (STI) and the detergent (β-octyl glucoside) (BOG) used in the experimental reservoir solution to obtain the crystal.

view = nglview.show_structure_file(download_pdb, default=True)
view.center()
view._remote_call('setSize', target='Widget', args=['','600px'])

view


Extract Protein Structure

Extract protein structure from the downloaded PDB file. Removing any extra molecule (ligands, ions, water molecules).

The protein structure will be used as a target in the protein-ligand docking process.


Building Blocks used:


from biobb_structure_utils.utils.extract_protein import extract_protein

pdb_protein = "pdb_protein.pdb"

extract_protein(input_structure_path=download_pdb,
             output_protein_path = pdb_protein)

Visualizing 3D structure

Visualizing the downloaded/given PDB structure using NGL.

Note that the small molecules included in the original structure are now gone. The new structure only contains the protein molecule, which will be used as a target for the protein-ligand docking.

view = nglview.show_structure_file(pdb_protein, default=True)
view.center()
view._remote_call('setSize', target='Widget', args=['','600px'])

view


Computing Protein Cavities (Cluster90 Binding Site)

Computing the protein cavities (pockets) using information from the PDB Cluster90. The PDB Cluster90 is a collection derived from the PDB database, containing structures having less than 90% sequence identity to each other. The Cluster90 Binding Site is using information from all the structures of the Cluster90 collection for a particular input protein to discover the possible binding sites from small molecules attached to similar proteins.

These cavities will be then used in the docking procedure to try to find the best region of the protein surface where the small molecule can bind.

Although in this particular example we already know the binding site region, as we started from a protein-ligand complex structure where the ligand was located in the same binding site as Imatinib is binding, this is not always the case. In the cases where we do not know these regions, the Cluster90 binding site utility will help us identifying the possible binding sites of our target protein.


Building Blocks used:


Getting Cluster 90 collection

Extracting the Cluster90 collection from the input PDB structure. The collection will contain all the structures in the PDB database having less than 90% sequence identity to the input structure (our target protein).

Please note that depending on the size of the cluster, the execution can take a while (minutes).

from biobb_io.api.pdb_cluster_zip import pdb_cluster_zip

pdb_cluster = "pdb_cluster.zip"
prop = {
    "pdb_code": pdb_code,
    "filter": ["ATOM", "HETATM"],
    "cluster": 90
}

pdb_cluster_zip(output_pdb_zip_path = pdb_cluster,
            properties=prop)

Extracting the Cluster90 Binding Site(s)

Extracting the protein binding site(s) from the Cluster90 collection. There are two ways in which the Cluster90 Binding Site can be used:

  • With information about the ligand: In this particular example we already know the ligand we want to dock, and also that the PDB database contain an experimental structure where the ligand was solved in complex with our protein of interest (p38-α Tyrosine kinase). In that case, the ligand id can be used to guide the binding site tool.

  • Without information about the ligand: If the docking study starts from a protein receptor with no information about the binding site or any known protein-ligand complex, the binding site tool can not be guided. Still, the tool should be able to extract binding site(s) information from similar proteins solved with small molecules attached (if any).

This example is using the ligand id to guide the binding site tool, but we invite you to try and explore the differences in the output removing this input information (ligand property in the next building block).

The Cluster90 binding site tool is internally running sequence alignments to structurally superpose the structures contained in the Cluster90 collection and extract the residue numbering corresponding to the binding site residues. Thus, sequence alignment parameters can be changed using building block input properties. In this example, we have chosen the well-known blosum62 substitution matrix, with a penalty for opening a gap of -10.0, and a penalty to extend a gap of -0.5. The total number of superimposed ligands to be extracted from the cluster is limited at 15, and the cut-off distance around the ligand atoms to consider a residue to be part of a binding site is fixed at 5 Ångstroms.

from biobb_vs.utils.bindingsite import bindingsite

output_bindingsite = "bindingsite.pdb"
prop = {
    "ligand": ligand_code,
    "matrix_name": "blosum62",
    "gap_open": -10.0,
    "gap_extend": -0.5,
    "max_num_ligands": 15,
    "radius": 5
}

bindingsite(input_pdb_path = pdb_protein,
            input_clusters_zip = pdb_cluster,
            output_pdb_path = output_bindingsite,
            properties=prop)

Visualizing selected pockets (cavities)

Visualizing the selected pockets (cavities) from the generated list using NGL viewer.

Protein residues forming the cavity are shown in licorice representation. Pockets are represented in a greyish surface. The original ligand (if exists) is shown in green-colored ball and stick representation.

view = nglview.show_structure_file(download_pdb, default=False)

# ligand
view[0].add_representation(repr_type='ball+stick', 
                          selection='STI',
                          aspect_ratio=4,
                          color='green')

view[0].add_representation(repr_type='cartoon', 
                        selection='not het',
                          opacity=.2,
                          color='#cccccc')

view.add_component(output_bindingsite, default=False)
view[1].add_representation(repr_type='surface', 
                           selection='*', 
                           opacity = .3,
                           radius='1.5',
                           lowResolution= True,
                           # 0: low resolution 
                           smooth=1,
                           useWorker= True,
                           wrap= True)
view[1].add_representation(repr_type='licorice', 
                        selection='*')

view[0].center()
view._remote_call('setSize', target='Widget', args=['','600px'])

view


Generating Cavity Box

Generating a box surrounding the selected protein cavity (pocket), to be used in the docking procedure. The box is defining the region on the surface of the protein target where the docking program should explore a possible ligand dock.
An offset of 12 Angstroms is used to generate a big enough box to fit the small molecule and its possible rotations.


Building Blocks used:

  • box from biobb_vs.utils.box


from biobb_vs.utils.box import box

output_box = "box.pdb"
prop = {
    "offset": 12,
    "box_coordinates": True
}

box(input_pdb_path = output_bindingsite,
            output_pdb_path = output_box,
            properties=prop)

Visualizing binding site box in 3D structure

Visualizing the protein structure, the selected cavity, and the generated box, all together using NGL viewer. Using the original structure with the small ligand inside (Imatinib, STI), to check that the selected cavity is placed in the same region as the original ligand.

#view = nglview.show_structure_file(box, default=False)
view = nglview.NGLWidget()
#s = view.add_component(pdb_single_chain)
s = view.add_component(download_pdb)
b = view.add_component(output_box)
s = view.add_component(output_bindingsite)

atomPair = [
    [ "9999:Z.ZN1", "9999:Z.ZN2" ],
    [ "9999:Z.ZN2", "9999:Z.ZN4" ],
    [ "9999:Z.ZN4", "9999:Z.ZN3" ],
    [ "9999:Z.ZN3", "9999:Z.ZN1" ],
    
    [ "9999:Z.ZN5", "9999:Z.ZN6" ],
    [ "9999:Z.ZN6", "9999:Z.ZN8" ],
    [ "9999:Z.ZN8", "9999:Z.ZN7" ],
    [ "9999:Z.ZN7", "9999:Z.ZN5" ],
    
    [ "9999:Z.ZN1", "9999:Z.ZN5" ],
    [ "9999:Z.ZN2", "9999:Z.ZN6" ],
    [ "9999:Z.ZN3", "9999:Z.ZN7" ],
    [ "9999:Z.ZN4", "9999:Z.ZN8" ]
]

#view.shape.add_cylinder( [ 0, 2, 7 ], [ 10, 0, 9 ], [ 1, 0, 0 ], 0.1 )

# structure
s.add_representation(repr_type='cartoon', 
                        selection='not het',
                        color='#cccccc',
                       opacity=.2)
# ligands box
b.add_representation(repr_type='ball+stick', 
                        selection='9999',
                        color='pink',
                       aspectRatio = 10)
# lines box
b.add_representation(repr_type='distance', 
                        atomPair= atomPair,
                       labelColor= 'transparent',
                       color= 'black')

# output bindingsite
s.add_representation(repr_type='surface', 
                        selection='*', 
                        color='skyblue',
                        lowResolution= True,
                        # 0: low resolution 
                        smooth=1,
                        surfaceType= 'av', 
                        contour=True,
                        opacity=0.4,
                        useWorker= True,
                        wrap= True)


view.center()
view._remote_call('setSize', target='Widget', args=['','600px'])

view


Downloading Small Molecule

Downloading the desired small molecule to be used in the docking procedure.
In this particular example, the small molecule of interest is the FDA-approved drug Imatinib, with PDB code STI.


Building Blocks used:


from biobb_io.api.ideal_sdf import ideal_sdf

sdf_ideal = "ideal.sdf"
prop = {
  "ligand_code": ligand_code
}

ideal_sdf(output_sdf_path=sdf_ideal,
    properties=prop)


Converting Small Molecule

Converting the desired small molecule to be used in the docking procedure, from SDF format to PDB format using the OpenBabel chemoinformatics tool.


Building Blocks used:


from biobb_chemistry.babelm.babel_convert import babel_convert

ligand = "ligand.pdb"
prop = {
    "input_format": "sdf",
    "output_format": "pdb",
    "obabel_path": "obabel"
}

babel_convert(input_path = sdf_ideal,
            output_path = ligand,
            properties=prop)


Preparing Small Molecule (ligand) for Docking

Preparing the small molecule structure for the docking procedure. Converting the PDB file to a PDBQT file format (AutoDock PDBQT: Protein Data Bank, with Partial Charges (Q), & Atom Types (T)), needed by AutoDock Vina.

The process adds partial charges and atom types to every atom. Besides, the ligand flexibility is also defined in the information contained in the file. The concept of “torsion tree” is used to represent the rigid and rotatable pieces of the ligand. A rigid piece (“root”) is defined, with zero or more rotatable pieces (“branches”), hanging from the root, and defining the rotatable bonds.

More info about PDBQT file format can be found in the AutoDock FAQ pages.


Building Blocks used:


from biobb_chemistry.babelm.babel_convert import babel_convert

prep_ligand = "prep_ligand.pdbqt"
prop = {
    "input_format": "pdb",
    "output_format": "pdbqt",
    "obabel_path": "obabel"
}

babel_convert(input_path = ligand,
            output_path = prep_ligand,
            properties=prop)

Visualizing small molecule (drug)

Visualizing the desired drug to be docked to the target protein, using NGL viewer.

  • Left panel: PDB-formatted file, with all hydrogen atoms.

  • Right panel: PDBqt-formatted file (AutoDock Vina-compatible), with united atom model (only polar hydrogens are placed in the structures to correctly type heavy atoms as hydrogen bond donors).

from ipywidgets import HBox

v0 = nglview.show_structure_file(ligand)
v1 = nglview.show_structure_file(prep_ligand)

v0._set_size('500px', '')
v1._set_size('500px', '')

def on_change(change):
    v1._set_camera_orientation(change['new'])
    
v0.observe(on_change, ['_camera_orientation'])

HBox([v0, v1])


Preparing Target Protein for Docking

Preparing the target protein structure for the docking procedure. Converting the PDB file to a PDBqt file, needed by AutoDock Vina. Similarly to the previous step, the process adds partial charges and atom types to every target protein atom. In this case, however, we are not taking into account receptor flexibility, although Autodock Vina allows some limited flexibility of selected receptor side chains (see the documentation).


Building Blocks used:


from biobb_structure_utils.utils.str_check_add_hydrogens import str_check_add_hydrogens

prep_receptor = "prep_receptor.pdbqt"
prop = {
    "charges": True,
    "mode": "auto"
}

str_check_add_hydrogens(input_structure_path = pdb_protein,
            output_structure_path = prep_receptor,
            properties=prop)


Running the Docking

Running the docking process with the prepared files:

  • ligand

  • target protein

  • binding site box

using AutoDock Vina.


Building Blocks used:


from biobb_vs.vina.autodock_vina import autodock_vina

output_vina_pdbqt = "output_vina.pdbqt"
output_vina_log = "output_vina.log"

autodock_vina(input_ligand_pdbqt_path = prep_ligand,
             input_receptor_pdbqt_path = prep_receptor,
             input_box_path = output_box,
            output_pdbqt_path = output_vina_pdbqt,
             output_log_path = output_vina_log)

Visualizing docking output poses

Visualizing the generated docking poses for the ligand, using NGL viewer.

  • Left panel: Docking poses displayed with atoms coloured by partial charges and licorice representation.

  • Right panel: Docking poses displayed with atoms coloured by element and ball-and-stick representation.

models = 'all'
#models = '/0 or /1 or /4'

v0 = nglview.show_structure_file(output_vina_pdbqt, default=False)
v0.add_representation(repr_type='licorice', 
                        selection=models,
                       colorScheme= 'partialCharge')
v0.center()
v1 = nglview.show_structure_file(output_vina_pdbqt, default=False)
v1.add_representation(repr_type='ball+stick', 
                        selection=models)
v1.center()

v0._set_size('500px', '')
v1._set_size('500px', '')

def on_change(change):
    v1._set_camera_orientation(change['new'])
    
v0.observe(on_change, ['_camera_orientation'])

HBox([v0, v1])

Select Docking Pose

Select a specific docking pose from the output list for visual inspection.
Choose a docking pose from the DropDown list.

from Bio.PDB import PDBParser
parser = PDBParser(QUIET = True)
structure = parser.get_structure("protein", output_vina_pdbqt)
models = []
for i, m in enumerate(structure):
    models.append(('model' + str(i), i))
    
mdsel = ipywidgets.Dropdown(
    options=models,
    description='Sel. model:',
    disabled=False,
)
display(mdsel)


Extract a Docking Pose

Extract a specific docking pose from the docking outputs.


Building Blocks used:


from biobb_vs.utils.extract_model_pdbqt import extract_model_pdbqt

output_pdbqt_model = "output_model.pdbqt"
prop = {
    "model": 1
}

extract_model_pdbqt(input_pdbqt_path = output_vina_pdbqt,
             output_pdbqt_path = output_pdbqt_model,
            properties=prop)


Converting Ligand Pose to PDB format

Converting ligand pose to PDB format.


Building Blocks used:


from biobb_chemistry.babelm.babel_convert import babel_convert

output_pdb_model = "output_model.pdb"
prop = {
    "input_format": "pdbqt",
    "output_format": "pdb",
    "obabel_path": "obabel"
}

babel_convert(input_path = output_pdbqt_model,
             output_path = output_pdb_model,
            properties=prop)


Superposing Ligand Pose to the Target Protein Structure

Superposing ligand pose to the target protein structure, in order to see the protein-ligand docking conformation.

Building a new PDB file with both target and ligand (binding pose) structures.


Building Blocks used:

  • cat_pdb from biobb_structure_utils.utils.cat_pdb


from biobb_structure_utils.utils.cat_pdb import cat_pdb

output_structure = "output_structure.pdb"

cat_pdb(input_structure1 = pdb_protein,
             input_structure2 = output_pdb_model,
             output_structure_path = output_structure)

Comparing final result with experimental structure

Visualizing and comparing the generated protein-ligand complex with the original protein-ligand conformation (downloaded from the PDB database), using NGL viewer.

  • Licorice, element-colored representation: Experimental pose.

  • Licorice, green-colored representation: Docking pose.


Note that outputs from AutoDock Vina don’t contain all the atoms, as the program works with a united-atom representation (i.e. only polar hydrogens).

view = nglview.NGLWidget()

# v1 = Experimental Structure
v1 = view.add_component(download_pdb)

v1.clear()
v1.add_representation(repr_type='licorice', 
                     selection='STI',
                     radius=0.5)

# v2 = Docking result
v2 = view.add_component(output_structure)
v2.clear()
v2.add_representation(repr_type='cartoon', colorScheme = 'sstruc')
v2.add_representation(repr_type='licorice', radius=0.5, color= 'green', selection='UNL')

view._remote_call('setSize', target='Widget', args=['','600px'])
view

# align reference and output
code = """
var stage = this.stage;
var clist_len = stage.compList.length;
var i = 0;
var s = [];
for(i = 0; i <= clist_len; i++){
    if(stage.compList[i] != undefined && stage.compList[i].structure != undefined) {        
       s.push(stage.compList[i])
    }
}
NGL.superpose(s[0].structure, s[1].structure, true, ".CA")
s[ 0 ].updateRepresentations({ position: true })
s[ 0 ].autoView()
"""

view._execute_js_code(code)

view


Questions & Comments

Questions, issues, suggestions and comments are really welcome!