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VS_Analysis: A Python package to perform post-virtual screening analysis

Dr. Muniba Faiza

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VS_Analysis: A Python package to perform post-virtual screening analysis

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.

Introduction

Welcome to the VS-Analysis Python package, a powerful toolkit designed to streamline the analysis of virtual screening results. This open-source package namely, VS_Analysis is a valuable resource for researchers and scientists engaged in the field of bioinformatics.

This package allows you to:

  • find the binding affinities of a selected number of docked compounds,
  • sort the binding affinities based on a binding affinity cut-off value (provided by the user) of a selected number of compounds,
  • find the specific binding affinity of a docked compound,
  • calculate the number of polar hydrogen bonds between the protein and the docked compounds including all poses,
  • calculate the distances of these hydrogen bonds, and
  • list the names of amino acid residues that are present near the ligand within a specified distance (default distance is 3.2 Angstroms).
  • identify whether a ligand is binding within a binding pocket or around a reference ligand in the protein or somewhere else.

Key Features

The VS-Analysis package encompasses several Python scripts, each tailored to facilitate specific aspects of result analysis. Let’s take a brief look at the core scripts:

  • vs_analysis.py:
    • A versatile script offering a comprehensive analysis of virtual screening results generated by Autodock Vina.
    • Provides insights into polar hydrogen bonds, distances, and other crucial parameters.
    • Simplifies the analysis process, particularly for large datasets.
  • vs_analysis_compounds.py:
    • Specialized script focusing on the analysis of compounds within the virtual screening results.
    • Extracts and presents detailed information about individual compounds, aiding in the identification of promising candidates.
  • vs_interaction_analysis.py:
    • precise insights into the dynamics between ligands and target molecules.
    • calculates the polar hydrogen bonds and their distances between the protein and the docked ligand.
    • lists all amino acids in the ligand affinity within a specified distance.
  • DockingAnalyzer.py:
    • Identification of relevant compounds involved in ligand binding.
    • Assessment of docking pose accuracy.
    • discard the compounds not docking inside the binding pocket.

    Usage

    The VS_Analysis package comprises Python scripts that are straightforward to execute. Please note to exclusively use Python 3; otherwise, these scripts may encounter errors. The results will be provided in separate output files.

    Availability

    The VS_Analysis package is freely accessible on our GitHub account.


    NOTE:

    • This script screens for the log files containing the word ‘log’ in their filenames, e.g., “log_compound1.txt” or “log_methanol.txt”
    • It is required to name your log files along with the name of a compound.

    How to cite:

    Faiza, M., (2024). VS_Analysis: A Python package to perform post-virtual screening analysis, 10(1): page 8-12. https://bioinformaticsreview.com/20240110/vs_analysis-a-python-package-to-perform-post-virtual-screening-analysis/

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

Bioinformatics Programming

DockingAnalyzer.py: A New Python script to Identify Ligand Binding in Protein Pockets.

Dr. Muniba Faiza

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DockingAnalyzer.py-A Python script to identify ligand binding in protein pockets.

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…)

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Proteins shape analyses reveal novel forms and connections.

Dr. Muniba Faiza

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Protein shape analyses reveal novel forms and connections.

A new study has revealed new forms of proteins that have never been seen before along with unexpected connections. Last year, Google DeepMind predicted the structure of all known proteins from organisms using AlphaFold [1].

(more…)

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What is MANE project?

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MANE is a collaborative project between NCBI and EMBL-EBI. It stands for Matched Annotation from NCBI and EMBL-EBI [1]. The datasets provided by NCBI and EMBL-EBI on the human genome are not identical across every gene.

(more…)

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This Month in Bioinformatics- Research Updates of March 2022

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