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In silico approach for the discovery of new PPARγ modulators among plant-derived polyphenols

Authors Encinar JA, Fernández-Ballester GJ, Galiano-Ibarra V, Micol V

Received 30 July 2015

Accepted for publication 23 September 2015

Published 4 November 2015 Volume 2015:9 Pages 5877—5895


Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Rekha Dhanwani

Peer reviewer comments 2

Editor who approved publication: Professor Wei Duan

José Antonio Encinar,1 Gregorio Fernández-Ballester,1 Vicente Galiano-Ibarra,2 Vicente Micol1,3

1Molecular and Cell Biology Institute, 2Physics and Computer Architecture Department, Miguel Hernández University, Elche, Spain; 3CIBER: CB12/03/30038 Physiopathology of Obesity and Nutrition, CIBERobn, Instituto de Salud Carlos III, Palma de Mallorca, Spain

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.

Keywords: virtual screening, molecular docking, high-throughput computing, TZDs, human PPARγ, AutoDock/Vina, ADMET, phenolic compounds

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