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Uncovering PPAR-γ agonists: An integrated computational approach driven by machine learning.

Sajjad Haider1, Muhammad Shafiq1, Ali Raza Siddiqui1

  • 1H. E. J. Research Institute of Chemistry, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, 75270, Pakistan.

Journal of Molecular Graphics & Modelling
|February 29, 2024
PubMed
Summary
This summary is machine-generated.

This study developed a computational approach combining machine learning and in silico drug design to identify selective modulators of Peroxisome proliferator-activated receptor gamma (PPAR-γ) for diabetes treatment, yielding promising drug candidates.

Keywords:
Machine learningMolecular dockingMolecular dynamic simulationPeroxisome proliferator activated receptor gammaType 2 diabetes mellitus

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Area of Science:

  • Biochemistry
  • Computational Chemistry
  • Pharmacology

Background:

  • Peroxisome proliferator-activated receptor gamma (PPAR-γ) is crucial for metabolic regulation and insulin sensitivity in diabetes.
  • Existing PPAR-γ agonists like Thiazolidinediones have significant side effects.
  • Selective PPAR-γ modulators offer a potential alternative with improved safety profiles.

Purpose of the Study:

  • To develop an integrated computational strategy using machine learning and in silico drug design to discover novel selective PPAR-γ modulators.
  • To identify compounds with high affinity and favorable interactions within the PPAR-γ ligand-binding site.
  • To assess the conformational stability of identified compounds in complex with PPAR-γ.

Main Methods:

  • Constructed a machine learning classification model trained on chemical and physicochemical descriptors of known PPAR-γ modulators.
  • Performed virtual screening of 31,750 compounds using the machine learning model.
  • Utilized molecular docking to evaluate binding affinity and interactions of selected compounds with PPAR-γ.
  • Analyzed molecular dynamics simulations to assess the stability and conformational changes of the PPAR-γ-ligand complexes.

Main Results:

  • Identified 68 potential PPAR-γ modulators from virtual screening, with four compounds selected for further analysis.
  • Docking scores for the top compounds ranged from -8.0 to -9.1 kcal/mol.
  • Key hydrogen bond interactions were observed with conserved residues (His323, Leu330, Phe363, His449, Tyr473) in the PPAR-γ binding site.
  • Molecular dynamics simulations indicated moderate conformational changes in the orthosteric site, with stability indices suggesting favorable interactions.

Conclusions:

  • The computational strategy successfully identified novel potential selective PPAR-γ modulators.
  • CHEMBL-3185642 and CHEMBL-3554847 demonstrated outstanding results, highlighting stable conformations within the PPAR-γ orthosteric site.
  • These identified compounds represent promising leads for developing safer and more effective diabetes therapies targeting PPAR-γ.