基于网络药理学和分子对接技术探讨生脉注射液抗新型冠状病毒肺炎的作用机制  

Study on mechanism of Shengmai Injection against novel coronavirus pneumonia based on network pharmacology and molecular docking technology

作　　者：王梁凤 李慧婷[3] 王堯 柳小莉 陈青垚 徐杰 杨明 张小飞[1,2] 王芳 WANG Liang-feng;LI Hui-ting;WANG Yao;LIU Xiao-li;CHEN Qing-yao;XU Jie;YANG Ming;ZHANG Xiao-fei;WANG Fang(Key Laboratory of Modern Preparation of Traditional Chinese Medicine of Ministry of Education,Jiangxi University of Traditional Chinese Medicine,Nanchang 330004,China;College of Pharmacy,Shaanxi University of Chinese Medicine,Xianyang 712046,China;College of Pharmacy,Chengdu University of Traditional Chinese Medicine,Chengdu 610075,China)

机构地区：[1]江西中医药大学现代中药制剂教育部重点实验室,江西南昌330004 [2]陕西中医药大学药学院,陕西咸阳712046 [3]成都中医药大学药学院,四川成都610075

出　　处：《中草药》2020年第11期2977-2987,共11页Chinese Traditional and Herbal Drugs

基　　金：国家自然科学基金资助项目(81960714);江西中医药大学双一流学科建设项目(JXSYLXK-ZHYAO083);江西中医药大学双一流学科建设项目(JXSYLXK-ZHYAO084)。

摘　　要：目的采用网络药理学与分子对接技术探讨生脉注射液的活性成分和治疗新型冠状病毒肺炎(COVID-19)的潜在作用机制。方法利用TCMSP及BATMAN-TCM数据库筛选生脉注射液的活性化合物,通过TCMSP及Targetnet在线数据库预测作用靶点,通过Cytoscape3.7.1构建活性成分-作用靶点网络图;在GeneCards及OMIM数据库中以"coronavirus pneumonia"为关键词搜索冠状病毒肺炎相关疾病靶点,与生脉注射液化合物靶点进行交集筛选出共同靶点作为研究靶点,将共同靶点导入STRING数据库获取数据后在Cytoscape 3.7.1软件中构建蛋白质-蛋白质相互作用网络图;利用R语言进行GO(gene ontology)功能、KEGG(Kyoto encyclopedia of genes and genomes)通路富集分析,预测其作用机制,并构建"成分-靶点-通路"网络图;通过DiscoveryStudio 2.5软件对关键靶点进行分子对接分析。结果生脉注射液筛选得到22个活性化合物,分别为邻苯二甲酸二辛酯、β-谷甾醇、当归酰基戈米辛O、戈米辛A、戈米辛R、五味子丙素、内南五味子酯乙、长南酸、南五味子内酯、香蒲木脂素B、新杜松烷酸A、新杜松烷酸B、新杜松烷酸C、新南五味子木脂宁、五味子内酯A、五味子内酯E、五味子酸、尿苷、薯蓣皂苷元、鸟嘌呤核苷、N-反式阿魏酰酪胺、豆甾醇。相应作用靶点224个,与COVID-19的共同靶点16个,分别为CASP3、CASP8、PTGS2、BCL2、BAX、PRKCA、PTGS1、PIK3CG、F10、NOS3、DPP4、NOS2、TLR9、ACE、ICAM1、PRKCE,关键靶点涉及CASP3、PTGS2、NOS2、NOS3、ICAM1。GO功能富集分析得到生物过程(BP)条目771个,细胞组成(CC)条目11个,分子功能(MF)条目79个。KEGG通路富集分析筛选得到67条(P<0.05)信号通路,主要涉及糖尿病并发症AGE-RAGE信号通路、凋亡通路、P53信号通路、小细胞肺癌通路等。分子对接结果显示与关键靶点对接较好的成分有五味子内酯E、豆甾醇、N-反式阿魏酰酪胺。结论生脉注射�Objective To explore the potential material basis of Shengmai Injection for the treatment of coronavirus disease 2019(COVID-19) through network pharmacology and molecular docking technology. Methods The active compounds of Shengmai Injection were screened by TCMSP and BATMAN-TCM database. The action target was predicted by TCMSP and Targetnet online database, and the active component-action target network diagram was constructed by Cytoscape 3.7.1;Taking "coronavirus pneumonia" as the keyword, coronavirus-related disease targets were searched in GeneCards database and OMIM database. The common target was selected by intersection with the target of Shengmai Injection as the research target. The common target was imported into STRING database to obtain data, and then the protein-protein interaction network map was constructed in Cytoscape 3.7.1 software;The enrichment analysis of GO function and KEGG pathway was carried out by using R language to predict its action mechanism and construct the "component-target-pathway" network diagram;Molecular docking analysis of key targets was carried out by DiscoveryStudio 2.5 software. Results A total of 22 active compounds were obtained from Shengmai Injection. They were DNOP, β-sitosterol, angeloylgomisin O, gomisin A, gomisin R, wuweizisu C, interiotherin B, changnanic acid, kadsulactone, kadsulignan B, neokadsuranic acid A, neokadsuranic acid B, neokadsuranic acid C, neokadsuranin, schisanlactone A, schisanlactone E, schizandronic acid, uridine, diosgenin, guanosine, N-trans-feruloyltyramine and stigmasterol. There were 224 corresponding targets and 16 common targets with COVID-19, namely CASP3, CASP8, PTGS2, BCL2, BAX, PRKCA, PTGS1, PIK3 CG, F10, NOS3, DPP4, NOS2, TLR9, ACE, ICAM1 and PRKCE. The key targets were CASP3, PTGS2, NOS2, NOS3 and ICAM1. GO functional enrichment analysis showed that there were 771 entries for biological processes, 11 entries for cell composition and 79 items for molecular function. A total of 67 signal pathways were screened by KEGG pathway enr

关 键 词：生脉注射液 新型冠状病毒肺炎 网络药理学 分子对接 KEGG通路富集分析 成分-靶点-通路 CASP3 PTGS2 NOS2 NOS3 ICAM1 五味子内酯E 豆甾醇 N-反式阿魏酰酪胺 抗炎 免疫调节 抗休克 增加血氧饱和度 

分 类 号：R285.5[医药卫生—中药学]