In Silico Identification of Potent PPAR- γ Agonists from Traditional Chinese Medicine: A Bioactivity Prediction, Virtual Screening, and Molecular Dynamics Study
Joint Authors
Chen, Calvin Yu-Chian
Chen, Kuan-Chung
Source
Evidence-Based Complementary and Alternative Medicine
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-19, 19 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-05-26
Country of Publication
Egypt
No. of Pages
19
Main Subjects
Abstract EN
The peroxisome proliferator-activated receptors (PPARs) related to regulation of lipid metabolism, inflammation, cell proliferation, differentiation, and glucose homeostasis by controlling the related ligand-dependent transcription of networks of genes.
They are used to be served as therapeutic targets against metabolic disorder, such as obesity, dyslipidemia, and diabetes; especially, PPAR- γ is the most extensively investigated isoform for the treatment of dyslipidemic type 2 diabetes.
In this study, we filter compounds of traditional Chinese medicine (TCM) using bioactivities predicted by three distinct prediction models before the virtual screening.
For the top candidates, the molecular dynamics (MD) simulations were also utilized to investigate the stability of interactions between ligand and PPAR- γ protein.
The top two TCM candidates, 5-hydroxy-L-tryptophan and abrine, have an indole ring and carboxyl group to form the H-bonds with the key residues of PPAR- γ protein, such as residues Ser289 and Lys367.
The secondary amine group of abrine also stabilized an H-bond with residue Ser289.
From the figures of root mean square fluctuations (RMSFs), the key residues were stabilized in protein complexes with 5-Hydroxy-L-tryptophan and abrine as control.
Hence, we propose 5-hydroxy-L-tryptophan and abrine as potential lead compounds for further study in drug development process with the PPAR- γ protein.
American Psychological Association (APA)
Chen, Kuan-Chung& Chen, Calvin Yu-Chian. 2014. In Silico Identification of Potent PPAR- γ Agonists from Traditional Chinese Medicine: A Bioactivity Prediction, Virtual Screening, and Molecular Dynamics Study. Evidence-Based Complementary and Alternative Medicine،Vol. 2014, no. 2014, pp.1-19.
https://search.emarefa.net/detail/BIM-1018213
Modern Language Association (MLA)
Chen, Kuan-Chung& Chen, Calvin Yu-Chian. In Silico Identification of Potent PPAR- γ Agonists from Traditional Chinese Medicine: A Bioactivity Prediction, Virtual Screening, and Molecular Dynamics Study. Evidence-Based Complementary and Alternative Medicine No. 2014 (2014), pp.1-19.
https://search.emarefa.net/detail/BIM-1018213
American Medical Association (AMA)
Chen, Kuan-Chung& Chen, Calvin Yu-Chian. In Silico Identification of Potent PPAR- γ Agonists from Traditional Chinese Medicine: A Bioactivity Prediction, Virtual Screening, and Molecular Dynamics Study. Evidence-Based Complementary and Alternative Medicine. 2014. Vol. 2014, no. 2014, pp.1-19.
https://search.emarefa.net/detail/BIM-1018213
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1018213