A promising in silico protocol to develop novel PPARγ antagonists as potential anticancer agents: Design, synthesis and experimental validation via PPARγ protein activity and competitive binding assay. (December 2021)

• Record Type: Journal Article
• Authors: Sivamani, Yuvaraj; Shanmugarajan, Dhivya; Durai Ananda Kumar, T.; Faizan, Syed; Channappa, Bhavya; Naishima, Namburu Lalitha; Prashantha Kumar, B.R.
• Is Part Of: Computational biology and chemistry. Volume 95(2021)
• Publication Date: 2021-12
• Subjects: 3DQSAR -- Pharmacophore -- PPARγ antagonist -- PPARγ protein activity -- Cancer; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2021.107600
• Languages: English
• ISSNs: 1476-9271

Abstract

Abstract: Peroxisome proliferator-activated receptor gamma (PPARγ), a member of the nuclear receptor superfamily is an excellent example of targets that orchestrates cancer, inflammation, lipid and glucose metabolism. We report a protocol for the development of novel PPARγ antagonists by employing 3D QSAR based virtual screening for the identification of ligands with anticancer properties. The models are generated based on a large and diverse set of PPARγ antagonist ligands by the HYPOGEN algorithm using Discovery Studio 2019 drug design software. Among the 10 hypotheses generated, Hypotheses 2 showed the highest correlation coefficient values of 0.95 with less RMS deviation of 1.193. Validation of the developed pharmacophore model was performed by Fischer's randomization and screening against test and decoy set. The GH score or goodness score was found to be 0.81 indicating moderate to a good model. The selected pharmacophore model Hypo 2 was used as a query model for further screening of 11, 145 compounds from the PubChem, sc-PDB structure database, and designed novel ligands. Based on fit values and ADMET filter, the final 10 compounds with the predicated activity of ≤ 3 nM were subjected for docking analysis. Docking analysis revealed the unique binding mode with hydrophobic amino acid that can cause destabilization of the H12 which is an important molecular mechanism to prove its antagonist action. Based on high CDocker scores, Cpd31 was synthesized, purified, analyzed and screened for PPARγ competitive binding by TR-FRET assay. The biochemical protein binding results matched the predicted results. Further, Cpd31 was screened against cancer cells and validated the results. Graphical Abstract: ga1 Highlights: 3D QSAR models are generated based on a large and diverse set of PPARγ antagonist ligands by the HYPOGEN algorithm. Pharmacophore models were generated to identify novel PPARγ antagonists and validated experimentally. Pharmacophore model based virtual screening protocol identified tyrosine derivatives as candidate compounds to investigate further. Compound 31 was synthesized, analysed by spectroscopy, evaluated for PPARγ binding studies and in vitro antiproliferative activity. Biological activity proves the model generated can be promising in silico approach in designing potent anticancer agents.