Jul 29, 2024
In Silico Molecular Docking with Ligand Target
- Angelo José Rinaldi1
- 1UFV
Protocol Citation: Angelo José Rinaldi 2024. In Silico Molecular Docking with Ligand Target. protocols.io https://dx.doi.org/10.17504/protocols.io.5jyl8j4wrg2w/v1
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License,  which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: May 18, 2023
Last Modified: July 29, 2024
Protocol Integer ID: 82104
Keywords: Molecular Docking, Enzyme-Ligand Interaction, Protein Data Bank, DrugBank, UCSF Chimera, MetaPocket 2.0, AutoDockTools, AutoDock Vina, ProteinsPlus, Bioinformatics, Drug Discovery., Cyclooxygenase, Celecoxib, silico molecular docking with ligand target, comprehensive molecular docking protocol, silico molecular docking, docking study, dimensional structure from the protein data bank, identification of potential ligand, ligand interaction, docking parameter file, ligand target, docking simulation, docking result, focus of the docking simulation, protein data bank, ligand, potential ligand, sites on the enzyme, crucial for drug discovery, affinities of the ligand, drugbank database, proteinsplus platform, various bioinformatics tool, leveraging various bioinformatics tool, using autodocktool, identification of key residue, studying enzyme, understanding of biochemical pathway, drug discovery, enzyme, autodock vina, preparation of the enzyme, detailed insights into the interaction profile, key residue, biochemical pathway,
Abstract
This study outlines a comprehensive molecular docking protocol aimed at exploring enzyme-ligand interactions, leveraging various bioinformatics tools and databases. The protocol commences with the acquisition of the enzyme's three-dimensional structure from the Protein Data Bank (PDB) and the identification of potential ligands from the DrugBank database. Structural refinement and preparation of the enzyme and ligands are conducted using UCSF Chimera, ensuring proper protonation states and the addition of missing atoms. MetaPocket 2.0 is employed to predict and identify potential binding sites on the enzyme, facilitating the focus of the docking simulations on relevant regions.
The prepared structures are then processed using AutoDockTools to generate the necessary input files, including the grid parameter file (GPF) and docking parameter file (DPF). AutoDock Vina is subsequently used to perform the docking simulations, predicting the binding poses and affinities of the ligands within the identified binding sites. The docking results are analyzed and visualized using the ProteinsPlus platform, providing detailed insights into the interaction profiles and enabling the identification of key residues involved in binding.
By integrating these diverse computational tools and databases, the protocol offers a robust framework for studying enzyme-ligand interactions, which is crucial for drug discovery and the understanding of biochemical pathways. The streamlined approach enhances the accuracy and efficiency of docking studies, facilitating the identification of promising therapeutic candidates.
Materials
Computational Tools and Software:
AutoDockTools (ADT):
Version: AutoDockTools 1.5.x
Usage: Preparation of the enzyme and ligand files, including the generation of PDBQT files and grid parameter files (GPF).
Download link: AutoDockTools
AutoDock Vina:
Version: AutoDock Vina 1.1.x
Usage: Execution of molecular docking simulations to predict binding poses and affinities.
Download link: AutoDock Vina
UCSF Chimera:
Version: UCSF Chimera 1.14 or later
Usage: Visualization, preparation, and refinement of the enzyme and ligand structures, including protonation and the addition of missing atoms.
Download link: UCSF Chimera
MetaPocket 2.0:
Version: MetaPocket 2.0
Usage: Prediction and identification of potential binding sites on the enzyme.
Access link: MetaPocket 2.0
ProteinsPlus:
Platform: ProteinsPlus web server
Usage: Analysis and visualization of docking results, including interaction profiling.
Access link: ProteinsPlus
Databases:
Protein Data Bank (PDB):
DrugBank:
Usage: Source of potential ligand molecules, including small molecules and drug-like compounds.
Access link: DrugBank
Input Files:
Enzyme Structure:
Format: PDB file
Source: Downloaded from the PDB database
Ligand Structures:
Format: PDB or MOL files
Source: Downloaded from the DrugBank database
Hardware:
Computer System:Processor: Multi-core CPU (e.g., Intel i7 or higher)
Memory: At least 16 GB RAM
Storage: Minimum 500 GB of free disk space
Operating System: Linux, Windows, or macOS
Miscellaneous:
Internet Access:
Required for downloading software, accessing databases, and using web-based tools like MetaPocket 2.0 and ProteinsPlus.
Troubleshooting
1) In this protocol we will use as an example of molecular docking, a target protein (receptor) called cyclooxygenase, which is an important enzyme in inflammatory processes. And as a ligand, the anti-inflammatory celecoxib.
Target receptor search:
2) Seeking the three-dimensional (3D) structure of the receiver from experimental data or computational models, here we will use the PDB.
3) We search the PDB for the enzyme called cyclooxygenase and choose the one with the best resolution, which in this case was 5F19.
4) Go in 3D view.
5) On the right tab, under display files, extract the receiver in pdb format and save it in a working folder.
Target ligand search:
6) If it is a known chemical structure use the Drugbank for the search, which we will use, if not, use chemical structure design software such as ACD/ChemSketch.
Search for the molecule under the name celecoxib.
7) In download save the celecoxib molecule in pdb format.
Preparation of the receptor:
8) Download the Chimera program from the link: https://www.cgl.ucsf.edu/chimera/download.html
9) Open the figure saved on the computer in Chimera or use the fetch id of 5F19
10) Visualization of the cyclooxygenase enzyme (receptor) in Chimera.
11) Go to select, go to residue and set to all non standard.
12) Residues that are not from the receiver were marked in green.
13) Clean the structure
14) Deleting a chain of amino acids to expose the ligand receptor.
15) The residues to be deleted are marked in green.
16) Save the file in pdb format.
17) MetaPocket 2.0 is an online tool used to predict binding sites on proteins. https://metapocket.eml.org/
Provide the structure of the prepared target protein in PDB (Protein Data Bank) format.
18) Select item 4 to choose the best results
19) Choose the first one that has the best result
20) Tools to prepare structures, such as AutoDockTools
Download: https://autodocksuite.scripps.edu/adt/
21) Add hydrogens to the molecule
22) Using the "polar only" option means that only polar hydrogens are considered when preparing the molecules.
23) Choose the prepared macromolecule to save
24) Configure the Grid Box with the parameters provided by metaPocket 2.0
25) Select the ligand
26) Save the celecoxib molecule in PDBQT format
27) Autodock Vina is molecular docking software and is generally used via the command line. The basic command to run Autodock Vina follows the following format:
vina --receptor <arquivo receptor.pdbqt> --ligand <arquivo ligante.pdbqt> --out <arquivo_saida.pdbqt>
28) Protein Plus The platform offers a series of integrated tools for protein and ligand analysis and visualization https://proteins.plus/
Choose the prepared files and click go.
29) After loading the file and configuring the parameters, start the analysis by clicking the appropriate button (for example, "Submit" or "Run PoseView").
The tool will process the protein-ligand complex and generate a diagram of the interactions.
30) After the analysis is completed, the tool will generate an interaction diagram. This diagram illustrates hydrogen bonds, hydrophobic interactions, and other important interactions between the protein and ligand.
Protocol references
1. Morris GM, Huey R, Lindstrom W, et al. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem. 2009;30(16):2785-2791.
2. Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31(2):455-461.
3. Meng XY, Zhang HX, Mezei M, Cui M. Molecular docking: a powerful approach for structure-based drug discovery. Curr Comput Aided Drug Des. 2011;7(2):146-157.
4. Cavasotto CN, Orry AJ, Murgolo NJ, Czarniecki MF, Kocsi SA, Hawes BE. Docking and high throughput docking: successes and pitfalls. J Chem Inf Model. 2009;49(4):1079-1093.
5. Kitchen DB, Decornez H, Furr JR, Bajorath J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov. 2004;3(11):935-949.
6. Shoichet BK. Virtual screening of chemical libraries. Nature. 2004;432(7019):862-865.