High-throughput virtual screening (HTVS) is a pivotal technique in drug discovery that screens extensive libraries of compounds to find potential drug candidates. One of the essential tasks in HTVS is to ensure that ligands are binding within the protein’s binding pocket. This task can be particularly challenging when dealing with thousands of docking results. To address this challenge, we present a Python script that automates the analysis of molecular docking results generated by AutoDock Vina [1] using PyMOL [2]. This script calculates the center of mass (COM) for each docked pose, compares it with a reference ligand’s COM, and identifies poses that bind within a specified threshold distance. This process is crucial in mass docking scenarios where confirming ligand binding within the pocket is necessary. (more…)

Copying and renaming files from one destination to another is easily accomplished by the cp command in Linux. However, it becomes more complex when we want to copy and rename all files in the same directory while assigning a serial number to each copy. In this article, we provide a shell script to copy and rename files present in a directory, adding a unique serial number to each copy.

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In molecular dynamics (MD) simulations, a free energy landscape (FEL) serves as a crucial tool for understanding the behavior of molecules and biomolecules over time. It is difficult to understand and plot a meaningful FEL and then extract the time frames at which the plot shows minima. In this article, we introduce a new Python package (Free_Energy_Landscape-MD) to generate an FEL based on principal component analysis (PCA) from MD simulation done by GROMACS [1].

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Virtual screening (VS) is a crucial aspect of bioinformatics. As you may already know, there are various tools available for this purpose, including both paid and freely accessible options such as Autodock Vina. Conducting virtual screening with Autodock Vina requires less effort than analyzing its results. However, the analysis process can be challenging due to the large number of output files generated. To address this, we offer a comprehensive Python package designed to automate the analysis of virtual screening results.

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Analyzing the results of virtual screening (VS) performed with Autodock Vina [1] can be challenging when done manually. In earlier instances, we supplied two scripts, namely vs_analysis.py [2,3] and vs_analysis_compounds.py [4]. This time, we have developed a new Python script to simplify the analysis of VS results.

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In this article. we are creating a pie chart of the docking score of five different compounds docked with the same protein. (more…)

With the new year, we are going to start with a very simple yet complicated topic (for beginners) in bioinformatics. In this tutorial, we provide a simple code to plot swarm boxplot using matplotlib and seaborn. (more…)

Previously, we provided a similar script to download ligand SMILES from PDB ligand IDs. In this article, we are downloading PDB ligand structures from their corresponding IDs. (more…)

Fetching SMILE strings for a given number of SDF files of chemical compounds is not such a trivial task. We can quickly obtain them using RDKit or OpenBabel. But what if you don’t have SDF files of ligands in the first place? All you have is Ligand IDs from PDB. If they are a few then you can think of downloading SDF files manually but still, it seems time-consuming, especially when you have multiple compounds to work with. Therefore, we provide a Python script that will read all Ligand IDs and fetch their SDF files, and will finally convert them into SMILE strings. (more…)

In this article, we will obtain the secondary structure of multiple PDB files present in a directory using DSSP [1]. You need to have DSSP installed on your system. (more…)

Previously, we have provided the vs_analysis.py script to analyze virtual screening (VS) results obtained from Autodock Vina. In this article, we have provided another script to search for binding affinity associated with a compound. (more…)

In this article, we provide a simple Python script to download files from an FTP server using Python. (more…)

VMD allows converting PDB to PSF format but sometimes it gives multiple errors. Therefore, in this article, we are going to convert PDB into PSF format using a different method. (more…)

As mentioned in some of our previous articles, RDKit provides a wide range of functions. In this article, we are using RDKit [1] to draw a molecular structure using SMILES. (more…)

Data preprocessing is a foremost and essential step in clustering based on machine learning methods. It removes noise and provides better results. In this article, we are going to discuss the steps involved in data preprocessing using MATLAB [1]. (more…)

RDKit [1] allows performing multiple functions on chemical compounds. One is the quantitative estimation of drug-likeness also known as QED properties. These properties include molecular weight (MW), octanol-water partition coefficient (ALOGP), number of hydrogen bond donors (HBD), number of hydrogen bond acceptors (HBA), polar surface area (PSA), number of rotatable bonds (ROTB), number of aromatic rings (AROM), structural alerts (ALERTS). (more…)

You can obtain SMILES of multiple compounds or ligands in an SDF file in one go. Here, we provide a simple Python script to do that. (more…)

We previously provided a Python script to calculate the Tanimoto similarities of multiple compounds against each other. In this article, we are providing another Python script to calculate the Tanimoto similarities of one compound with multiple compounds. (more…)

RDKit [1] is a very nice cheminformatics software. It allows us to perform a wide range of operations on chemical compounds/ ligands. We have provided a Python script to perform fingerprinting using Tanimoto similarity on multiple compounds using RDKit. (more…)

In this article, we are providing a few commands that are used to commit changes to GitHub repositories using VS code terminal.

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