Publikasi Scopus 2024 per tanggal 30 April 2024 (334 artikel)

Astuti P.D.Y.; Fadilah F.; Promsai S.; Bahtiar A.
Astuti, Purnama Dewi Yuli (58990865800); Fadilah, Fadilah (56966708600); Promsai, Saran (55260128600); Bahtiar, Anton (35365874400)
58990865800; 56966708600; 55260128600; 35365874400
Integrating molecular docking and molecular dynamics simulations to evaluate active compounds of Hibiscus schizopetalus for obesity
2024
Journal of Applied Pharmaceutical Science
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4
176
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Department of Pharmacology and Toxicology, Faculty of Pharmacy, Universitas Indonesia, Depok, Indonesia; Department of Medicinal Chemistry, Faculty of Medicine, Universitas Indonesia, Depok, Indonesia; Division of Microbiology, Department of Science, Faculty of Liberal Arts and Science, Kasetsart University—KU KPS, Nakhon Pathom, Thailand
Astuti P.D.Y., Department of Pharmacology and Toxicology, Faculty of Pharmacy, Universitas Indonesia, Depok, Indonesia; Fadilah F., Department of Medicinal Chemistry, Faculty of Medicine, Universitas Indonesia, Depok, Indonesia; Promsai S., Division of Microbiology, Department of Science, Faculty of Liberal Arts and Science, Kasetsart University—KU KPS, Nakhon Pathom, Thailand; Bahtiar A., Department of Pharmacology and Toxicology, Faculty of Pharmacy, Universitas Indonesia, Depok, Indonesia
The group screened and identified the content of Hibiscus schizopetalus by protein–protein interaction, molecular docking, and dynamics as a potential therapy for obesity through pancreatic lipase (PNLIP) as a protein target. First, the group collected all active ingredients of H. schizopetalus from an online database (http://www.knapsackfamily.com/ and http://ijah.apps.cs.ipb.ac.id/) to identify and isolate active compounds. The 3-D structures and canonical of the active compound were taken from the PubChem database, and then all compounds were analyzed by pkCSM and Tox-Protox II to get pharmacokinetics and physical-chemistry properties. The protein target of obesity was identified using the Open Target Platform. After the protein targets of plant extract and obesity were collected, the group analyzed them using Cytoscape. Protein–protein interaction was analyzed using String, Gene ontology, and KEGG pathway. Virtual screening was done by Pyrx software, and visualization was done by Discovery Studio Biovia, proceed by molecular docking using AutoDockTools-1.5.7, and finally, molecular dynamics (MDs) was done using YASARA software. The group collected 70 compounds from a research journal and found 196 protein targets. The target of obesity was 165 protein targets. The 196 protein targets of H. schizopetalus and 165 protein targets were analyzed and merged using Cytoscape and 11 proteins targeting H. schizopetalus and obesity. After that, the group analyzed which compound of H. schizopetalus affected 11 protein targets by Pyrx with the highest binding affinity. PNLIP has the highest binding affinity compared to other proteins, so the group analyzed this PNLIP protein with its relationship to obesity. The group found that three proteins that work on PNLIP are beta-sitosterol, kaempferol, and gallocatechin gallate. After docking these three proteins, the group found only one active compound has the highest binding affinity compared to the commercial drug Orlistat. Then, the process ended by performing MDs of the active compound as a candidate drug for anti-obesity. In this study, the group found that gallocatechin gallate, as an active compound of H. schizopetalus, can inhibit PNLIP enzymes for obesity therapy by bio-informatics study. © 2024 Purnama Dewi Yuli Astuti et al. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
bioinformatics methods; H. schizopetalus; Natural products; pancreatic lipase; protein–protein interaction
apigenin; behenic acid; Cleomiscosin; Feruloyltyramine; gallic acid; gallocatechin; kaempferol; linoleic acid; linolenic acid; malvidin chloride; Mansonone H; pelargonidin; phytochemical; plant extract; sitosterol; stearic acid; tetrahydrolipstatin; triacylglycerol lipase; unclassified drug; Article; binding affinity; bioinformatics; CA2 gene; CNR1 gene; computer model; CREBBP gene; EP300 gene; ESR1 gene; ESR2 gene; gene ontology; Hibiscus; Hibiscus schizopetalus; KEGG; machine learning; molecular docking; molecular dynamics; obesity; pharmacokinetics; physical chemistry; phytochemistry; PNLIP gene; protein protein interaction; PTGS1 gene; PTGS2 gene; signal transduction; VDR gene
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