与蛋白质靶点相关的活性贮藏研究&硅钙土的研究

Moch Mustakim, Vina Fitria Wulandari, Nabilah Miftachul Khoiriyah, Azis Mawardi, R. Wulandari, Thiya Fathiyatul Fauziyah, Nurul Jadid Mubarakati
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引用次数: 1

摘要

摘要阿尔茨海默病是2019年世界卫生组织公布的十大致命神经退行性疾病。其特征是胆碱乙酰转移酶(ChAt)的减少,这种物质在产生asetikolin中起作用。已知自然界的一种成分在增加对pegagan的记忆中起作用。为了了解用于治疗阿尔茨海默氏症的活性化合物机制,该研究使用了一种计算机方法,通过分析化合物网络上的靶蛋白和化合物的活性化合物。这项研究使用了12种测试化合物和ChAt受体分子。网络设计使用海洋目标和字符串数据库这些化合物的相互作用。这种相互作用的结果在细胞景观设备上可视化。此外,结果还表明,八种化合物的测试与ChAt受体有相互作用,而与ChAt受体直接连接的靶蛋白是色氨酸5-羟化酶1(PDB ID:5tpg)和锌指蛋白GLI(PDB ID:4kmd)。因此,与蛋白质靶标作用最紧密的pegagan活性化合物是亚油酸、婆罗米酸、-胡木烯、-石竹烯、双环锗马烯、锗马烯B、-派恩、石竹烯。关键词:阿尔茨海默病,胆碱乙酰转移酶(ChAt),聚乙二醇,计算机,网络相互作用
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INVESTIGASI BAHAN AKTIF PEGAGAN BERDASARKAN JEJARING DENGAN PROTEIN TARGET : STUDI PENCARIAN OBAT ALZHEIMER SECARA IN SILICO
  ABSTRACT Alzheimer’s disease is a top-10 deadly neurodegenerative disease based on WHO in 2019. Its characterized by a reduction in Choline Acetyltransferase (ChAt) of a substance that acts in the production of asetikolin. One of the ingredients of nature known to play a part in the increased memory of pegagan. To learn about the line of active compound mechanisms used in the treatment of alzheimer's, the study uses an in silico approach by analyzing target proteins and the compound's active compound on a network of compounds. This study used 12 test compounds and the ChAt receptor molecules. Networking design the interactions of these compounds using the sea target and STRING databases. The results of that interactions are visualized on the cytoscape device. Furthermore, the results also suggested thateight of compounds’stest that have interactions with ChAt receptors whereas the target proteins directly linked to ChAt receptors namely Tryptophan 5-hydroxylase 1 (PDB ID: 5tpg)  and Zinc finger protein GLI (PDB ID: 4kmd). Thus, the pegagan active compound that acts most closely on protein target is Asiatic Acid, Brahmic Acid, -Humulene, -Caryophyllene, Bicyclogermacrene, Germacrene B, -Pinene, Caryophyllene. Keywords: Alzheimer's disease, Choline Acetyltransferase (ChAt), pegagan, in silico, network interaction
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