Computational identification of potential chemoprophylactic agents according to dynamic behavior of peroxisome...

Area of Science:

• Computational chemistry and drug discovery
• Molecular modeling and simulation
• Pharmacology and chemoprevention

Background:

• Peroxisome proliferator-activated receptor gamma (PPARγ) is a key target for lung cancer chemoprevention.
• Drug development for PPARγ has been hindered by challenges in receptor modeling due to its dynamic nature.
• Understanding PPARγ's conformational flexibility is crucial for effective structure-based drug design.

Purpose of the Study:

• To apply an integrated virtual screening strategy to identify PPARγ agonists for lung cancer chemoprevention.
• To investigate the impact of ligand binding on PPARγ's conformational dynamics, particularly key loops and surfaces.
• To discover novel chemoprophylactic agents targeting PPARγ.

Main Methods:

• Employed a receptor-based virtual screening (VS) approach integrating extensive docking and conformational sampling.
• Analyzed the conformational plasticity of PPARγ, focusing on the H2 & S245 loop, H2' & Ω loop, and AF-2 surface.
• Screened commercial agents for their ability to modulate PPARγ dynamics and AF-2 surface flexibility.

Main Results:

• The conformational plasticity of PPARγ is significantly influenced by the binding of full and partial agonists.
• Identified five commercial agents with antineoplastic properties, including ZINC03775146 (gusperimus) and ZINC14087743 (miltefosine) as potential novel PPARγ agonists.
• These compounds exhibit flexible switching of the AF-2 surface and state changes in the Ω loop, indicating potential chemoprophylactic activity.

Conclusions:

• The integrated VS strategy effectively accounts for PPARγ's dynamic behavior, aiding drug discovery for flexible targets.
• Gusperimus and miltefosine show promise as PPARγ agonists for lung cancer chemoprevention.
• The identified dynamic structural couplings offer new avenues for developing PPARγ-targeted therapeutics.