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基于网络药理学和分子对接技术解析清解方预防新型冠状病毒肺炎的作用机制

Decoding the mechanism of Qingjie formula in the prevention of COVID-19 based on network pharmacology and molecular docking.

作者信息

Pan Yu, Lin Wanchun, Huang Yueyue, Pan Jingye, Dong Yihua

机构信息

Department of Pharmacy, Zhejiang Chinese Medical University Affiliated Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine, Wenzhou, Zhejiang, 325000, China.

Department of Intensive Care Unit, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China.

出版信息

Heliyon. 2024 Oct 11;10(20):e39167. doi: 10.1016/j.heliyon.2024.e39167. eCollection 2024 Oct 30.

Abstract

Traditional Chinese medicine (TCM) has played a positive role in preventing and controlling the coronavirus disease 2019 (COVID-19) epidemic. Qingjie formula (QJF) developed to prevent COVID-19 is widely used in Wenzhou, Zhejiang province, China. However, the biological active ingredients of QJF and their specific mechanisms for preventing COVID-19 remain unclear. The study focused on exploring the pharmacological mechanism of QJF for the prevention of COVID-19 based on network pharmacology and molecular docking. The active ingredients of QJF were screened by TCMSP database. Databases such as Genecards and Swiss Target Prediction predicted potential targets of QJF against COVID-19. The "drug-active ingredient-potential target" network was constructed by Cytoscape software. We used STRING database to construct the protein-protein interaction (PPI) network. Enrichment of biological functions and signaling pathways were analyzed by using the DAVID database and R language. Then AutoDock Vina and Python software were used for molecular docking of hub targets and active ingredients. 147 active ingredients interacted with 316 potential targets of COVID-19. A PPI network consisting of 30 hub genes was constructed, and the top 10 hub genes were ALB, AKT1, TP53, TNF, IL6, VEGFA, IL1B, CASP3, JUN and STAT3. The results of GO analysis showed that these targets were mainly enriched in cell responses to oxidative stress, chemical stress, and other functions. KEGG analysis revealed that viral protein interactions with cytokines (e.g., human cytomegalovirus infection), endocrine resistance pathways (e.g., AGE-RAGE signaling pathway), PI3K-Akt signaling pathway, and lipid and atherosclerosis signaling pathway were the major signaling pathways. Moreover, the core active ingredients of QJF had good binding affinity with hub genes by molecular docking. QJF plays an important role in the prevention of COVID-19 by regulating host immune inflammatory response and oxidative stress response, inhibiting virus, improving immune function, regulating the hypoxia-cytokine storm, and inhibiting cell migration.

摘要

中医药在预防和控制新型冠状病毒肺炎(COVID-19)疫情中发挥了积极作用。为预防COVID-19而研发的清解方(QJF)在中国浙江省温州市被广泛使用。然而,QJF的生物活性成分及其预防COVID-19的具体机制仍不清楚。本研究基于网络药理学和分子对接,重点探索QJF预防COVID-19的药理机制。通过中药系统药理学数据库(TCMSP)筛选QJF的活性成分。利用基因卡片(Genecards)和瑞士靶点预测(Swiss Target Prediction)等数据库预测QJF抗COVID-19的潜在靶点。使用Cytoscape软件构建“药物-活性成分-潜在靶点”网络。我们利用STRING数据库构建蛋白质-蛋白质相互作用(PPI)网络。使用DAVID数据库和R语言分析生物学功能和信号通路的富集情况。然后使用AutoDock Vina和Python软件对核心靶点和活性成分进行分子对接。147种活性成分与316个COVID-19潜在靶点相互作用。构建了一个由30个核心基因组成的PPI网络,排名前10的核心基因是白蛋白(ALB)、蛋白激酶B1(AKT1)、肿瘤蛋白p53(TP53)、肿瘤坏死因子(TNF)、白细胞介素-6(IL6)、血管内皮生长因子A(VEGFA)、白细胞介素-1β(IL1B)、半胱天冬酶3(CASP3)、原癌基因c-Jun(JUN)和信号转导和转录激活因子3(STAT3)。基因本体(GO)分析结果表明,这些靶点主要富集在细胞对氧化应激、化学应激等反应中。京都基因与基因组百科全书(KEGG)分析显示,病毒蛋白与细胞因子的相互作用(如人巨细胞病毒感染)、内分泌抵抗途径(如晚期糖基化终末产物-受体(AGE-RAGE)信号通路)、磷脂酰肌醇-3激酶-蛋白激酶B(PI3K-Akt)信号通路以及脂质和动脉粥样硬化信号通路是主要的信号通路。此外,通过分子对接发现QJF的核心活性成分与核心基因具有良好的结合亲和力。QJF通过调节宿主免疫炎症反应和氧化应激反应、抑制病毒、改善免疫功能、调节缺氧-细胞因子风暴以及抑制细胞迁移,在预防COVID-19中发挥重要作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/204b/11620151/a6a3fa46a8db/gr1.jpg

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