Mapping the Allosteric Landscape of PPAR_γ: a Markov State Modeling and Energetic Analysis Approach

Area of Science:

• Biochemistry
• Computational Biology
• Pharmacology

Background:

• Peroxisome proliferator-activated receptor γ (PPARγ) is crucial for metabolic regulation, with its function dependent on ligand-induced conformational dynamics.
• The precise molecular mechanisms by which diverse ligands selectively influence PPARγ's conformational states are not fully understood.

Purpose of the Study:

• To elucidate the dynamic allostery governing PPARγ activation and inhibition using an integrative computational approach.
• To identify key residues and molecular interactions responsible for ligand-mediated conformational selection in PPARγ.
• To develop and validate a computational framework for the rational design of selective PPARγ modulators.

Main Methods:

• Extensive molecular dynamics (MD) simulations and Markov state modeling (MSM) were employed to analyze PPARγ's conformational landscape.
• Binding free energy calculations were performed to quantify ligand-receptor interactions and thermodynamic hierarchies.
• MSM-guided virtual screening of the ZINC20 database was used for prospective validation.

Main Results:

• Ligand binding was shown to reshape the PPARγ conformational landscape by altering dynamic hubs in the R2 and R3 regions.
• Antagonists demonstrated the highest binding affinity, primarily through hydrophobic interactions restricting Helix 12 mobility.
• Key residues Arg288 and Ile341 were identified as critical nodes in the allosteric network, and two natural compounds were identified as potential modulators.

Conclusions:

• This study provides atomistic insights into the conformational selection mechanism of PPARγ, driven by ligand-induced dynamic allostery.
• The findings highlight the importance of specific residues and hydrophobic interactions in modulating receptor activity.
• The developed computational framework is transferable for investigating allosteric regulation in nuclear receptors and designing novel therapeutic agents.