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Virtual Screening Methodology for Structure-based Drug Designing

Dr. Muniba Faiza

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Virtual Screening Methodology

Virtual High Throughput Screening (vHTS), also known as Virtual Screening (VS) is one of the essential steps involved in in-silico drug designing. There are several bioinformatics tools that facilitate the virtual screening of thousands of compounds such as GOLD, GLIDE, Autodock Vina, and so on.

Despite all the processing done by these tools, a basic methodology is required for vHTS of thousands of compounds present in a database. In this article, we are presenting a few basic steps required for performing structure-based VS using bioinformatics tools. The complete schema is presented in Figure 1.

Figure 1 Basic steps involved in virtual screening.

Step 1. Prepare receptor

Download the structure of receptor protein from PDB. In case, the structure is not available, predict the structure using homology modeling or ab-initio methods of structure prediction. Search the literature to gather information about the binding pocket or binding residues of the protein of interest. Select a chain (or two) consisting of the binding region. If the structural details are not available in the literature, then go for binding pocket prediction using webservers or tools.

Step 2. Download compound databases such as the ZINC database of small molecules.

Download the .sdf or .mol2 files of compounds. Some databases allow downloading only smiles of the compounds, then download the smiles and use a web server or software to convert these smiles into .sfd/.mol2 files.

Step 3. Prepare receptor and ligands for docking

Depending upon the software you are using for VS, prepare the files of the receptor protein and the ligands. for example, Autodock Vina requires receptor and ligand files in .pdbqt format.

Step 4. Run VS

Load all the files in the software and run VS. Wait for the results.

Step 5. Select drug-like compounds

Select the drug-like molecules based on the binding affinity and interaction with binding residues. You can also select some lead-like molecules showing less binding affinity.

Step 6. Filtering

You can further filter obtained drug-like molecules based on their toxicity, ADME properties, poses, and so on.

Step 7. Compound selection

Select potential drug candidates for further in vitro experiments.

These are the basic steps involved in VS for structure-based drug designing. There are several bioinformatics tools other than those mentioned above that are used in VS including Discovery Studio, LigandScout, and so on.

Dr. Muniba is a Bioinformatician based in New Delhi, India. She has completed her PhD in Bioinformatics from South China University of Technology, Guangzhou, China. She has cutting edge knowledge of bioinformatics tools, algorithms, and drug designing. When she is not reading she is found enjoying with the family. Know more about Muniba

Docking

How to perform metal ion-protein docking using idock?

Dr. Muniba Faiza

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How to perform metal ion-protein docking using idock?

Previously, we provided a tutorial on the installation of idock on Ubuntu (Linux). In this article, we are going to demonstrate the docking of a metal ion (such as Zn, Mg, Fe, etc.,) with a protein using idock. (more…)

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Docking

How to install idock on Ubuntu?

Dr. Muniba Faiza

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How to install idock on Ubuntu?

idock [1] is a multithreaded software based on Autodock Vina. It is a virtual screening tool for flexible ligand docking. It also supports 27 different chemical elements including zinc, magnesium, iron, calcium, etc. In this article, we are going to install idock on Ubuntu. (more…)

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Docking

How to analyze HADDOCK results using Pymol script generated from PRODIGY?

Dr. Muniba Faiza

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How to analyze HADDOCK results using Pymol script generated from PRODIGY?

In one of our previously published articles, we demonstrated protein-protein docking using HADDOCK2.4 [1]. In this article, we are going to demonstrate the HADDOCK results analysis using a Pymol script generated from the PRODIGY server [2]. (more…)

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