基于网络药理学及分子对接探讨淡豆豉抗抑郁的作用机制

目的 采用网络药理学和分子对接技术预测淡豆豉抗抑郁活性成分、作用靶点及通路,探讨其潜在作用机制。方法 利用CNKI、TCMSP、TCMIP检索并筛选淡豆豉抗抑郁潜在活性成分;运用PharmMapper服务器预测活性成分的作用靶点;利用UniProt数据库查询靶点蛋白对应的基因名;运用DrugBank、GeneCards和DisGeNET数据库检索抗抑郁作用靶基因,并将活性成分靶点相互映射获得淡豆豉活性成分抗抑郁靶点;通过String数据库下载靶蛋白相互作用数据,与获取的潜在作用靶点利用Cytoscape 3.7.2.软件构建淡豆豉–活性成分–潜在靶点相互作用网络图。运用DAVID数据库分析潜在靶点的基因本体（GO）分子功能和京都基因与基因组百科全书（KEGG）信号通路,最后利用Autodock Vina和Pymol软件对药物有效活性成分和关键靶点进行分子对接验证。结果 预测获得淡豆豉主要活性成分8个,作用靶点396个,与抑郁症交集潜在靶点334个,GO分子功能和KEGG通路涉及多种生物学过程以及脂质和动脉粥样硬化通路、磷脂酰肌醇-3激酶/蛋白激酶B（PI3K-Akt）信号通路、小分子量G蛋白（Ras）信号通路、叉头框蛋白（FoxO）信号通路等多个信号通路。将关键活性成分和靶点进行对接,其中丝氨酸-苏氨酸蛋白激酶1（AKT1）与大豆苷元结合能力较好;磷酸肌醇3-激酶调节亚基1（PIK3R1）与木犀草素、槲皮素结合能力较好;HSP90-α热休克蛋白（HSP90AA1）与黄豆黄素结合能力较好。结论 淡豆豉以多种活性成分、多个作用靶点和途径发挥其抗抑郁作用,为淡豆豉在临床上用于抑郁症的干预和治疗提供依据。

Mechanism of Sojae Semen Praeparatum in treatment of anti-depression based on network pharmacology and molecular docking

Objective Network pharmacology and molecular docking techniques were used to predict the anti-depression active components, action targets and pathways of Sojae Semen Praeparatum (SSP), and to explore its potential mechanism of action. Methods CNKI, TCMSP and TCMIP were used to search and screen the potential antidepressant active ingredients of SSP. PharmMapper server was used to predict the target of active ingredients. UniProt database was used to query the gene name corresponding to target protein. DrugBank, GeneCards and DisGeNET databases were used to search the target genes for anti-depressant action, and the target of SSP was mapped to each other. Target protein interaction data were downloaded from String database, with Cytoscape 3.7.2 being used to obtain potential targets. The interaction network diagram of SSP-active component-potential target was constructed by software. DAVID database was used to analyze the molecular functions of gene body (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways of potential targets. Finally, Autodock Vina and Pymol software were used to conduct molecular docking verification for effective active ingredients and key targets of drugs. Results Eight main active components of SSP, 396 targets and 334 potential intersection targets with depression were predicted. GO molecular function and KEGG pathway involve a variety of biological processes, lipid and atherosclerosis pathways, Ras signaling pathway, PI3K-Akt signaling pathway, FoxO signaling pathway and other signaling pathways. Key active ingredients and targets were docked, among which AKT1 had better binding ability to Daidzein. PIK3R1 had better binding ability to luteolin and quercetin. HSP90AA1 had better binding ability to glycitein.Conclusion SSP exerts its anti-depressant effect through multiple active components, targets and pathways, providing evidence for SSP to be used in clinical depression on intervention and treatment.