title: In silico approach for the discovery of new PPARγ modulators among plant-derived polyphenols.
abstract: Peroxisome proliferator-activated receptor gamma (PPARγ) is a well-characterized member of the PPAR family that is predominantly expressed in adipose tissue and plays a significant role in lipid metabolism, adipogenesis, glucose homeostasis, and insulin sensitization. Full agonists of synthetic thiazolidinediones (TZDs) have been therapeutically used in clinical practice to treat type 2 diabetes for many years. Although it can effectively lower blood glucose levels and improve insulin sensitivity, the administration of TZDs has been associated with severe side effects. Based on recent evidence obtained with plant-derived polyphenols, the present in silico study aimed at finding new selective human PPARγ (hPPARγ) modulators that are able to improve glucose homeostasis with reduced side effects compared with TZDs. Docking experiments have been used to select compounds with strong binding affinity (ΔG values ranging from -10.0±0.9 to -11.4±0.9 kcal/mol) by docking against the binding site of several X-ray structures of hPPARγ. These putative modulators present several molecular interactions with the binding site of the protein. Additionally, most of the selected compounds have favorable druggability and good ADMET properties. These results aim to pave the way for further bench-scale analysis for the discovery of new modulators of hPPARγ that do not induce any side effects.
authors.affiliation: Molecular and Cell Biology Institute, Miguel Hernández University, Elche, Spain.
authors.forename: José Antonio
authors.initials: JA
authors.lastname: Encinar
authors.affiliation: Molecular and Cell Biology Institute, Miguel Hernández University, Elche, Spain.
authors.forename: Gregorio
authors.initials: G
authors.lastname: Fernández-Ballester
authors.affiliation: Physics and Computer Architecture Department, Miguel Hernández University, Elche, Spain.
authors.forename: Vicente
authors.initials: V
authors.lastname: Galiano-Ibarra
authors.affiliation: Molecular and Cell Biology Institute, Miguel Hernández University, Elche, Spain ; CIBER: CB12/03/30038 Physiopathology of Obesity and Nutrition, CIBERobn, Instituto de Salud Carlos III, Palma de Mallorca, Spain.
authors.forename: Vicente
authors.initials: V
authors.lastname: Micol
mesh_terms: D001665:Binding Sites; D001786:Blood Glucose / Q000187:drug effects; D003198:Computer Simulation; D003924:Diabetes Mellitus, Type 2 / Q000188:drug therapy* / Q000503:physiopathology; D015195:Drug Design; D006801:Humans; D007004:Hypoglycemic Agents / Q000009:adverse effects / Q000302:isolation & purification / Q000494:pharmacology*; D007328:Insulin / Q000378:metabolism; D007333:Insulin Resistance; D062105:Molecular Docking Simulation; D047495:PPAR gamma / Q000187:drug effects* / Q000378:metabolism; D010946:Plants, Medicinal / Q000737:chemistry; D059808:Polyphenols / Q000009:adverse effects / Q000302:isolation & purification / Q000494:pharmacology*; D045162:Thiazolidinediones / Q000494:pharmacology
chemical_list: D001786:Blood Glucose; D007004:Hypoglycemic Agents; D007328:Insulin; D047495:PPAR gamma; D059808:Polyphenols; D045162:Thiazolidinediones
keywords: ADMET; AutoDock/Vina; TZDs; high-throughput computing; human PPARγ; molecular docking; phenolic compounds; virtual screening