目的 采用網(wǎng)絡(luò)藥理學(xué)和分子對接技術(shù)預(yù)測淡豆豉抗抑郁活性成分、作用靶點(diǎn)及通路，探討其潛在作用機(jī)制。方法 利用CNKI、TCMSP、TCMIP檢索并篩選淡豆豉抗抑郁潛在活性成分；運(yùn)用PharmMapper服務(wù)器預(yù)測活性成分的作用靶點(diǎn)；利用UniProt數(shù)據(jù)庫查詢靶點(diǎn)蛋白對應(yīng)的基因名；運(yùn)用DrugBank、GeneCards和DisGeNET數(shù)據(jù)庫檢索抗抑郁作用靶基因，并將活性成分靶點(diǎn)相互映射獲得淡豆豉活性成分抗抑郁靶點(diǎn)；通過String數(shù)據(jù)庫下載靶蛋白相互作用數(shù)據(jù)，與獲取的潛在作用靶點(diǎn)利用Cytoscape 3.7.2.軟件構(gòu)建淡豆豉–活性成分–潛在靶點(diǎn)相互作用網(wǎng)絡(luò)圖。運(yùn)用DAVID數(shù)據(jù)庫分析潛在靶點(diǎn)的基因本體（GO）分子功能和京都基因與基因組百科全書（KEGG）信號通路，最后利用Autodock Vina和Pymol軟件對藥物有效活性成分和關(guān)鍵靶點(diǎn)進(jìn)行分子對接驗(yàn)證。結(jié)果 預(yù)測獲得淡豆豉主要活性成分8個，作用靶點(diǎn)396個，與抑郁癥交集潛在靶點(diǎn)334個，GO分子功能和KEGG通路涉及多種生物學(xué)過程以及脂質(zhì)和動脈粥樣硬化通路、磷脂酰肌醇-3激酶/蛋白激酶B（PI3K-Akt）信號通路、小分子量G蛋白（Ras）信號通路、叉頭框蛋白（FoxO）信號通路等多個信號通路。將關(guān)鍵活性成分和靶點(diǎn)進(jìn)行對接，其中絲氨酸-蘇氨酸蛋白激酶1（AKT1）與大豆苷元結(jié)合能力較好；磷酸肌醇3-激酶調(diào)節(jié)亞基1（PIK3R1）與木犀草素、槲皮素結(jié)合能力較好；HSP90-α熱休克蛋白（HSP90AA1）與黃豆黃素結(jié)合能力較好。結(jié)論 淡豆豉以多種活性成分、多個作用靶點(diǎn)和途徑發(fā)揮其抗抑郁作用，為淡豆豉在臨床上用于抑郁癥的干預(yù)和治療提供依據(jù)。

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.

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國家自然科學(xué)基金項(xiàng)目(U20A20400);黑龍江自然科學(xué)基金項(xiàng)目(LH2019H094);藥食同源中藥發(fā)酵關(guān)鍵技術(shù)創(chuàng)新中心建設(shè)項(xiàng)目(CZKYF2021A002)