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Biotechnology

GC-MS Analysis of phytochemical compounds from Javan olive leaves (Olea javanica) extract and bioactivity screening of antidiabetes through in silico approach

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Pages 52-63

Abstract

Diabetes is one of the most common metabolic diseases with the ninth highest mortality rate in the world. Although there are many effective hypoglycemic drugs available for the treatment of diabetes, researchers are constantly looking for more effective drugs with fewer side effects by focusing on various metabolic components such as enzymes, transporters, receptors. The Glucokinase (HGKA) enzyme, which is mainly found in the liver and pancreatic beta cells, is involved in maintaining blood glucose homeostasis. Therefore, the purpose of this study on GC-MS and in silico analysis was designed to determine the interaction between Glucokinase and the compound (ligand) of Java olive leaf extract (Olea javanica). The method used in this study was maceration with 96% ethanol to produce Java olive leaf extract, then analyzed by GC-MS using the Agilent 6980N Network GC System chromatography, then in silico analysis was carried out using the PyRx 0.9 software, pkCSM website, Biovia Discovery Studio 2019, RCSB PDB database, BIOVIA Discovery Studio software, AutoDock Vina, CABS-flex 2.0 web server, and Lipinski's rules. The results of the GC-MS analysis showed compounds in the form of 3-methylpentane, Hexane, Methylcyclopentane, Alpha-muurolene, -(-calamenene), Methyl 14-methylpentadecanoid acid, Methyl ester linoleic acid, Trans squalene, Alpha tocopherol. In silico analysis revealed a molecule that matched the target protein for the treatment of diabetes in the form of the compound -(-calamenene) which has antidiabetic activity based on molecular docking in this study.

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How to Cite This

Fitri, L., Wulandari, A. D., Sumaryono, T., Haibaturrahma, N. F. A., Agustina, B. N., & Tamam, M. B. (2024). GC-MS Analysis of phytochemical compounds from Javan olive leaves (Olea javanica) extract and bioactivity screening of antidiabetes through in silico approach . Jurnal Teknologi Laboratorium, 13(1), 52–63. https://doi.org/10.29238/teknolabjournal.v13i1.467

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