基于网络药理学探讨预知子抗抑郁的作用机制

目的:运用网络药理学的方法揭示预知子抗抑郁的作用机制。方法:采用TCMSP、PharmMapper、Swiss TargetPrediction和GeneCards数据库挖掘预知子的活性成分及其抗抑郁的作用靶点，采用String数据库获取蛋白-蛋白相互作用关系，运用Cytoscape软件构建预知子的成分-作用靶点和PPI网络，利用DAVID数据库对关键靶点的GO和KEGG信号通路进行富集分析。最后，采用AutoDockTools-1.5.6 软件进行分子对接验证。结果:筛选得到预知子的木通苯乙醇B、豆甾醇葡萄糖苷、齐墩果酸等6个核心活性成分，EGFR、MAPK1/8、SRC、HSP90AA1、AR等8个重要抗抑郁靶点。参与调控的16条抑郁相关的信号通路包括催乳素信号通路（Prolactin signaling pathway）、ErbB信号通路（ErbB signaling pathway）、GnRH信号通路（GnRH signaling pathway）、黏着斑（Focal adhesion）等。分子对接结果显示预知子核心活性成分与靶点具有较好的结合活性。结论:预知子抗抑郁的作用机制可通过多成分-多靶点-多通路的综合作用而实现。

Mechanism of Anti-depression Mechanism of Akebiae Fructus Based on Network Pharmacology

Objective: Revealing the anti-depression mechanism of Akebiae Fructus by network pharmacology technology. Methods: The active compounds and its corresponding depression-related targets of Akebiae Fructus were mined from the TCMSP, PharmMapper, Swiss TargetPrediction, and GeneCards databases. The protein-protein interactions were gained from the String database. The compound-target and PPI networks were built by Cytoscape software. The DAVID database was exploited for enrichment analysis of GO and KEGG signaling pathways for key targets. Finally, molecular docking was carried out for verification using AutoDockTools-1.5.6 software. Results: The 6 active compounds of Akebiae Fructus were identified, including calceolarioside B, stigmasterol glucoside, and oleanolic acid, etc. 8 major depression-related targets were predicted, such as EGFR, MAPK1/8, SRC, HSP90AA1, AR, etc. 16 depression-related signaling pathways were modulated, namely the prolactin signaling pathway, ErbB signaling pathway, GnRH signaling pathway, focal adhesion, etc. The results of molecular docking showed that the kernel components had good binding activity with the key targets. Conclusion: Akebiae Fructus exerted anti-depression effect through the comprehensive combination of multiple components, multiple targets and multiple pathways.