PPARγ non-covalent antagonists exhibit mutable binding modes with a similar free energy of binding: a case study

International Union of Basic and Clinical Pharmacology CXIII: Nuclear Receptor Superfamily-Update 2023

The NR superfamily comprises 48 transcription factors in humans that control a plethora of gene network programs involved in a wide range of physiologic processes. This review will summarize and discuss recent progress in NR biology and drug development derived from integrating various approaches, including biophysical techniques, structural studies, and translational investigation. We also highlight how defective NR signaling results in various diseases and disorders and how NRs can be targeted for therapeutic intervention via modulation via binding to synthetic lipophilic ligands. Furthermore, we also review recent studies that improved our understanding of NR structure and signaling. SIGNIFICANCE STATEMENT: Nuclear receptors (NRs) are ligand-regulated transcription factors that are critical regulators of myriad physiological processes. NRs serve as receptors for an array of drugs, and in this review, we provide an update on recent research into the roles of these drug targets.

Benzimidazoles Containing Piperazine Skeleton at C-2 Position as Promising Tubulin Modulators with Anthelmintic and Antineoplastic Activity

Benzimidazole anthelmintic drugs hold promise for repurposing as cancer treatments due to their interference with tubulin polymerization and depolymerization, manifesting anticancer properties. We explored the potential of benzimidazole compounds with a piperazine fragment at C-2 as tubulin-targeting agents. In particular, we assessed their anthelmintic activity against isolated Trichinella spiralis muscle larvae and their effects on glioblastoma (U-87 MG) and breast cancer (MDA-MB-231) cell lines. Compound 7c demonstrated exceptional anthelmintic efficacy, achieving a 92.7% reduction in parasite activity at 100 μg/mL after 48 hours. In vitro cytotoxicity analysis of MDA-MB 231 and U87 MG cell lines showed that derivatives 7b, 7d, and 7c displayed lower IC50 values compared to albendazole (ABZ), the control. These piperazine benzimidazoles effectively reduced cell migration in both cell lines, with compound 7c exhibiting the most significant reduction, making it a promising candidate for further study. The binding mode of the most promising compound 7c, was determined using the induced fit docking-molecular dynamics (IFD-MD) approach. Regular docking and IFD were also employed for comparison. The IFD-MD analysis revealed that 7c binds to tubulin in a unique binding cavity near that of ABZ, but the benzimidazole ring was fitted much deeper into the binding pocket. Finally, the absolute free energy of perturbation technique was applied to evaluate the 7c binding affinity, further confirming the observed binding mode.

Computational identification of potential chemoprophylactic agents according to dynamic behavior of peroxisome proliferator-activated receptor gamma

Peroxisome proliferator-activated receptor gamma (PPARγ) is an attractive target for chemoprevention of lung carcinoma, however its highly dynamic nature has plagued drug development for decades, with difficulties in receptor modeling for structure-based design. In this work, an integrated receptor-based virtual screening (VS) strategy was applied to identify PPARγ agonists as chemoprophylactic agents by using extensive docking and conformational sampling methods. Our results showed that the conformational plasticity of PPARγ, especially the H2 & S245 loop, H2' & Ω loop and AF-2 surface, is markedly affected by binding of full/partial agonists. To fully take the dynamic behavior of PPARγ into account, the VS approach effectively sorts out five commercial agents with reported antineoplastic properties. Among them, ZINC03775146 (gusperimus) and ZINC14087743 (miltefosine) might be novel PPARγ agonists with the potential for chemoprophylaxis, that simultaneously take part in a flexible switch of the AF-2 surface and state change of the Ω loop. Furthermore, the dynamic structural coupling between the H2 & S245 and H2' & Ω loops offers enticing hope for PPARγ-targeted therapeutics, by blocking kinase accessibility to PPARγ. These results might aid the development of chemopreventive drugs, and the integrated VS strategy could be conducive to drug design for highly flexible biomacromolecules.

Prediction of Accurate Binding Modes Using Combination of Classical and Accelerated Molecular Dynamics and Free-Energy Perturbation Calculations: An Application to Toxicity Studies

Estimating the correct binding modes of ligands in protein-ligand complexes is crucial not only in the drug discovery process but also for elucidating potential toxicity mechanisms. In the current paper, we propose a computational modeling workflow using the combination of docking, classical molecular dynamics (cMD), accelerated molecular dynamics (aMD) and free-energy perturbation (FEP+ protocol) for identification of possible ligand binding modes. It was applied for investigation of selected perfluorocarboxyl acids (PFCAs) in the PPARγ nuclear receptor. Although both regular and induced fit docking failed to reproduce the experimentally determined binding mode of the ligands when docked into a non-native X-ray structure, cMD and aMD simulations successfully identified the most probable binding conformations. Moreover, multiple binding modes were identified for all of these compounds and the shorter-chain PFCAs continuously moved between a few energetically favorable binding conformations. On the basis of MD predictions of binding conformations, we applied the default and also redesigned FEP+ sampling protocols, which accurately reproduced experimental differences in the binding energies. Thus, the preliminary MD simulations can also provide helpful information about correct setup of the FEP+ calculations. These results show that the PFCA binding modes were accurately predicted and that the FEP+ protocol can be used to estimate free energies of binding of flexible ligands that are not typical druglike compounds. Our in silico workflow revealed the specific ligand-residue interactions within the ligand binding domain and the main characteristics of the PFCAs, and it was concluded that these compounds are week PPARγ partial agonists. This work also suggests a common pipeline for identification of ligand binding modes, ligand-protein dynamics description, and relative free-energy calculations.

PPARγ helix 12 exhibits an antagonist conformation

PPARγ activation helix 12 can exist in an antagonist form: evidence from high-throughput accelerated molecular dynamics and metadynamics.