Zhang Ziyou, Cheng Jiamao, Zhou Xinpei, Wu Haoyi, Zhang Bensi
Dali University, College of Basic Medicine, Dali, 671000, China.
Heliyon. 2024 Aug 16;10(16):e36471. doi: 10.1016/j.heliyon.2024.e36471. eCollection 2024 Aug 30.
This study aimed to investigate the mechanism of Tu Fu Ling in treating Alzheimer's disease (AD) using network pharmacology and molecular docking.
The TCMSP and Swiss target prediction databases were utilized to confirm the active components of Tu Fu Ling and their corresponding targets, with target gene names converted using the UniProt database. Genes related to AD were collected from DisGeNET, GeneCards, and the Open Target Platform databases. Common target genes between the disease and the drug were obtained using Venny 2.1 tools and visualized using Cytoscape software. Protein-protein interaction (PPI) data were further analyzed to determine correlations between common target genes, and GO and KEGG pathway enrichment analyses were performed for intersecting genes. Finally, PYmol, AutoDock Tool, Discovery Studio 2020, and PyRx software were used for preliminary computer virtual verification and visualization of active drug ingredients and target proteins.
Nine active ingredients meeting the screening criteria yielded a total of 168 genes after removing duplicates. A total of 3833 target genes were collected, with 129 overlapping target genes identified. GO enrichment analysis identified 643 biological processes, 82 cellular components, and 147 molecular functions. KEGG pathway enrichment analysis also revealed a pathway closely related to AD (hsa05010: Alzheimer's disease). In molecular docking analysis, the binding affinity between the 9 active ingredients and 10 core targets ranged from -3.5 to -12.3 kcal/mol, indicating strong binding.
This study preliminarily verified the combination of Tu Fu Ling's screened active ingredient and the calculated core target, suggesting a potential mechanism of action to improve the symptoms of AD patients through multi-target and multi-pathway approaches. This provides a valuable reference for further exploration of the pharmacological mechanism of AD and the formulation of drug therapy.
本研究旨在运用网络药理学和分子对接技术探讨土茯苓治疗阿尔茨海默病（AD）的机制。
利用中药系统药理学数据库与分析平台（TCMSP）和瑞士靶点预测数据库确定土茯苓的活性成分及其相应靶点，并使用UniProt数据库转换靶点基因名称。从疾病基因数据库（DisGeNET）、基因卡片（GeneCards）和开放靶点平台数据库收集与AD相关的基因。使用Venny 2.1工具获得疾病与药物之间的共同靶点基因，并使用Cytoscape软件进行可视化。进一步分析蛋白质-蛋白质相互作用（PPI）数据以确定共同靶点基因之间的相关性，并对交集基因进行基因本体（GO）和京都基因与基因组百科全书（KEGG）通路富集分析。最后，使用PYmol、自动对接工具、Discovery Studio 2020和PyRx软件对活性药物成分和靶蛋白进行初步计算机虚拟验证和可视化。
9种符合筛选标准的活性成分去除重复后共产生168个基因。共收集到3833个靶点基因，鉴定出129个重叠靶点基因。GO富集分析确定了643个生物学过程、82个细胞成分和147个分子功能。KEGG通路富集分析还揭示了一条与AD密切相关的通路（hsa05010：阿尔茨海默病）。在分子对接分析中，9种活性成分与10个核心靶点之间的结合亲和力范围为-3.5至-12.3千卡/摩尔，表明结合力较强。
本研究初步验证了土茯苓筛选出的活性成分与计算得到的核心靶点的结合，提示通过多靶点、多途径改善AD患者症状的潜在作用机制。这为进一步探索AD的药理机制和药物治疗方案的制定提供了有价值的参考。
