Identification of novel peroxisome proliferator-activated receptor-gamma (PPARγ) agonists using molecular modeling method
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Peroxisome proliferator-activated receptor-gamma (PPARγ) plays a critical role in lipid and glucose homeostasis. It is the target of many drug discovery studies, because of its role in various disease states including diabetes and cancer. Thiazolidinediones, a synthetic class of agents that work by activation of PPARγ, have been used extensively as insulin-sensitizers for the management of type 2 diabetes. In this study, a combination of QSAR and docking methods were utilised to perform virtual screening of more than 25 million compounds in the ZINC library. The QSAR model was developed using 1,517 compounds and it identified 42,378 potential PPARγ agonists from the ZINC library, and 10,000 of these were selected for docking with PPARγ based on their diversity. Several steps were used to refine the docking results, and finally 30 potentially highly active ligands were identified. Four compounds were subsequently tested for their in vitro activity, and one compound was found to have a K i values of <5 μM.
KeywordsPPARγ QSAR Docking
This work was supported by the Ministry of Education Academic Research Fund Tier 1 grant [R-148-000-165-112] to CWY. APK is supported by grants from the Singapore Ministry of Education Tier 2 [MOE2012-T2-2-139], Academic Research Fund Tier 1 [R-184-000-228-112], Cancer Science Institute of Singapore, Experimental Therapeutics I Program [Grant R-713-001-011-271], NUHS Bench-to-Beside-Product Grant [184-000-243-515] and John Nott Cancer Fellowship from Cancer Council, Western Australia. GS was supported by grants from National Medical Research Council of Singapore [R-184-000-211-213]. We would like to thank the two reviewers for their insightful and encouraging comments, which helped to improve the quality of our manuscript greatly.
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