神经药理学报

神经药理学报 ›› 2024, Vol. 14 ›› Issue (2): 31-.DOI: 10.3969/j.issn.2095-1396.2024.02.006

• 研究论文 • 上一篇    下一篇

基于网络药理学和分子对接探究槲皮素治疗阿尔茨海默病的作用机制

王天旭，刘慈，崔永元，张鑫，吴苗苗，沈丽霞   

  1. 河北北方学院药学院，河北省神经药理学重点实验室，张家口，075000，中国
  • 出版日期:2024-04-26 发布日期:2024-07-11
  • 作者简介:王天旭，研究方向：网络药理学
  • 基金资助:
    河北省高等学校科学技术研究项目（No.ZC2023064），2023年河北省大学生创新创业训练计划省级项目（N o . S202310092013），河北北方学院校级科研项目（No.2023039）

Explore the Mechanism of Quercetin in the Treatment of Alzheimer's  Disease by Network Pharmacology and Molecular Docking

WANG Tian-xu, LIU Ci, CUI Yong-yuan, ZHANG Xin, WU Miao-miao, SHEN Li-xia   

  1. Department of Pharmacy, Hebei North University, Hebei Key Laboratory of Neuropharmacology, Zhangjiakou, 075000, China
  • Online:2024-04-26 Published:2024-07-11

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摘要：

目的：基于网络药理学和分子对技术探究槲皮素治疗阿尔茨海默病（Alzheimer's disease，AD）的作 用机制。方法：利用中药系统药理学数据库与分析平台（Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform，TCMSP）、PubChem 数据库和Swiss Target Prediction 平台预测槲皮素潜在靶点， 利用Disgenet 数据库获得AD 的潜在靶点。通过维恩图对槲皮素潜在靶点和AD 潜在靶点取交集得到共同靶点， 将共同靶点导入DAVID 数据库，以P<0.05 进行筛选，进行基因本体（gene ontology, GO）分析和京都基因与基 因百科全书（Kyoto Encyclopedia of Genes and Genomes，KEGG）通路富集分析。运用 STRING 数据库构建蛋白 质- 蛋白质相互作用(protein-protein interaction, PPI) 网络，通过Cytoscape 3.6.0 软件的 centiscape 2.2 插件对 PPI 网络进行分析，得到槲皮素治疗阿尔茨海默病的关键靶点。将关键靶点按照Degree 从大到小排序取前5 为 核心靶点，使用SailVina final 软件对筛选出的核心靶点进行分子对接。结果：三个数据库得到槲皮素潜在靶点 有319 个、阿尔茨海默病潜在靶点有673 个，交集靶点有92 个，筛选出的关键靶点有23 个，GO 分析中生物过程 主要有基因表达正调控、细胞凋亡过程的正调控、对外源性刺激的反应、细胞凋亡过程的负调控等。KEGG 通路 分析主要富集在IL-17、HIF-1、FoxO、TNF、PI3K-Akt、MAPK 等信号通路。槲皮素与核心靶点IL-6、AKT、TP53、 TNF、IL-1β 分子对接的平均对接亲和力为 -7.92 kcal·mol-1，结合活性较好。结论：利用网络药理学和分子对接 技术预测了槲皮素治疗AD 的潜在靶点及信号通路，为后续实验研究提供理论基础。

关键词: 槲皮素, 阿尔茨海默病, 网络药理学, 分子对接

Abstract:

Objective: To investigate the mechanism of quercetin in the treatment of Alzheimer's disease based on network pharmacology and molecular docking. Methods: Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform, PubChem database and Swiss Target Prediction platform were used to predict potential targets of quercetin. Potential targets for Alzheimer's disease were obtained through the Disgenet database. Common targets were obtained by intersection of potential quercetin targets and potential Alzheimer's disease targets through Venn diagram. The common targets were imported into DAVID database for screening at P<0.05, and gene ontology(GO) analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis were performed. The protein-protein interaction (PPI) network was constructed utilizing STRING database. The centiscape 2.2 plug-in of Cytoscape 3.6.0 software was used to analyze the PPI network, and the key target of quercetin in the treatment of Alzheimer’s disease was obtained. The top 5 key targets will be selected as the core targets in order of degree from largest to smallest, and the selected core targets will be subjected to molecular docking with SailVina final software. Results: There were 319 potential targets of quercetin in the three databases, 673 potential targets of Alzheimer's disease, 92 intersection targets, and 23 key targets screened out. The biological processes in GO analysis mainly included positive regulation of gene expression, positive regulation of apoptosis, response to exogenous stimuli, and negative regulation of apoptosis. KEGG pathway analysis mainly concentrated in IL-17, HIF-1, FoxO, TNF, PI3K-Akt, MAPK signaling pathway and so on. The average docking affinity between quercetin and core target molecules was -7.92 kcal·mol-1. Quercetin has good binding activity with the core targets IL-6, AKT, TP53, TNF and IL-1β. Conclusion: The potential targets and signaling pathways of quercetin in the treatment of AD have been found by network pharmacology and molecular docking techniques, which provided theoretical basis for subsequent experimental studies.

Key words: quercetin, Alzheimer's disease, network pharmacology, molecular dockings

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