通过网络药理学和分子对接发现的银杏叶活性化合物可调节急性山地病和缺血性中风的发生发展

iLABMED Pub Date : 2024-09-03 DOI:10.1002/ila2.58
Haoran Guo, Xueran Kang, Ying Xu, Chengbin Wang, Chi Wang
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引用次数: 0

摘要

背景 采用分子对接、分子动力学模拟和中药网络药理学相结合的方法,研究银杏叶活性化合物对急性山地病(AMS)和缺血性中风(IS)预防作用的共同关键靶点和潜在机制。 材料与方法 利用中药系统药理学数据库和分析平台筛选银杏叶的主要活性化合物及其相应靶点。我们通过挖掘多个数据库获得 AMS 相关基因,并将这些基因与银杏叶的主要活性化合物进行交叉关联,以确定治疗 AMS 的相关作用靶点。我们利用 STRING 数据库构建了银杏叶活性化合物对 AMS 靶点影响的蛋白质-蛋白质相互作用网络。在 IS 数据集中分析了网络中基因的表达,以确定银杏叶活性化合物在预防 AMS 和 IS 方面的共同关键靶点。 结果 银杏叶活性化合物的靶点与 AMS 相关基因之间的交叉发现了 43 个重叠基因。蛋白相互作用网络分析显示,VEGFA、TP53、SERPINE1 和 PTGS2 是关键的枢纽基因。对 IS 数据集的分析发现,CAT、TP53、CXCL8、NFKBIA 和 PTGS2 的表达水平存在显著差异。这些基因被用于构建IS预后的可视化提名图预测模型,具有良好的临床意义。分子对接和分子动力学模拟表明,芝麻素能稳定地靶向结合 PTGS2。 结论 银杏叶的活性成分,包括木犀草素、槲皮素和芝麻素,有可能通过与 TP53、CXCL8、NFKBIA、PTGS2 和 CAT 等关键蛋白的靶向相互作用,调节 AMS 和 IS 的发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Ginkgo biloba active compounds can modulate the development of acute mountain sickness and ischemic stroke as discovered by network pharmacology and molecular docking

Background

A combination of molecular docking, molecular dynamics simulations, and herbal network pharmacology was used to investigate the shared key targets and potential mechanisms underlying the preventive effects of Ginkgo biloba active compounds against acute mountain sickness (AMS) and ischemic stroke (IS).

Material and Methods

The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform was used to screen the main active compounds of Ginkgo biloba and their corresponding targets. We obtained AMS-related genes by mining several databases and cross-correlated them with key active compounds of Ginkgo biloba to identify relevant action targets for treating AMS. The STRING database was used to construct a protein–protein interaction network of the effect of Ginkgo biloba active compounds on AMS targets. The expression of genes in the network was analyzed in an IS dataset to identify common key targets of Ginkgo biloba active compounds for both AMS and IS prevention.

Results

The intersection between the targets of Ginkgo biloba active compounds and AMS-related genes identified 43 overlapping genes. Analysis of the protein–protein interaction network showed that VEGFA, TP53, SERPINE1, and PTGS2 were among the key hub genes. Analysis of the IS dataset identified significant differences in the expression levels of CAT, TP53, CXCL8, NFKBIA, and PTGS2. These genes were used to construct a visual nomogram prediction model for IS prognosis with promising clinical implications. Molecular docking and molecular dynamics simulations indicated that sesamin stably targeted and bound to PTGS2.

Conclusions

Active ingredients of Ginkgo biloba, including luteolin, quercetin, and sesamin, have the potential to modulate the development of AMS and IS through targeted interactions with key proteins, including TP53, CXCL8, NFKBIA, PTGS2, and CAT.

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