目的 基于超高效液相色谱-四极杆飞行时间串联质谱（UPLC-Q-TOF-MS/MS）、网络药理学、分子对接及分子动力学模拟探究夏枯草茎叶总酚抗炎有效成分及其作用机制。方法 采用UPLC-Q-TOF-MS/MS技术对夏枯草茎叶水提物中成分进行分析；使用Swiss Target Prediction、GeneCards和OMIM数据库筛选夏枯草抗炎作用对应的靶点；使用STRING数据库和Cytoscape软件构建关键靶点蛋白相互作用（PPI）网络；通过Metascape数据库对关键靶点进行基因本体论（GO）功能与京都基因与基因组百科全书（KEGG）信号通路富集分析；通过TCMSP、PDB数据库对已鉴定的成分与核心靶点进行分子对接；采用amber18软件包，取对接结合能前3位的对接复合物分别进行200 ns的分子动力学模拟。结果 共鉴定出异迷迭香酸苷、紫草酸、染料木素、槲皮素和丹参酚酸Y等22个化合物，其中酚酸类16种，黄酮类6种。基于鉴定出的化合物通过网络药理学得到502个潜在抗炎靶点，PPI分析发现肿瘤蛋白（TP53）、信号传导和转录激活蛋白3（STAT3）、转录因子AP-1（JUN）、低氧诱导因子-1A（HIF1A）、黏着连接蛋白β1（CTNNB1）、半胱氨酸天冬氨酸蛋白酶-3（CASP3）、肿瘤坏死因子（TNF）等为核心靶点，富集分析发现核心靶点可能通过调节程序性死亡受体1（PD-1）、白细胞介素-17（IL-17）和晚期糖基化终末产物（AGE）/AGEs受体（RAGE）等信号通路发挥抗炎作用，分子对接与分子动力学模拟实验表明已鉴定出的成分与关键靶点均能自由结合，结合能前3位的分子与受体蛋白复合物具有较稳定构象，结合后不会导致其构象发生持续的、显著的改变。结论 基于UHPLC-Q-TOF-MS/MS技术和网络药理学可实现对夏枯草茎叶抗炎物质基础挖掘和机制预测，有助于夏枯草非药用部位资源的开发利用。

Objective To explore the effective components of Prunella vulgaris stem and leaf total phenols and their anti-inflammatory mechanisms based on UPLC-Q-TOF-MS/MS, network pharmacology, molecular docking, and molecular dynamics simulation. Methods UHPLC-Q-TOF-MS/MS was used to analyze the total phenols in the aqueous extract of Prunella vulgaris stem and leaf. Using databases such as Swiss Target Prediction, GeneCards, and OMIM to screen the target corresponding to the anti-inflammatory effect of Prunella vulgaris. The key target protein interaction (PPI) network was constructed using STRING database and Cytoscape software. GO function and KEGG signal pathway enrichment analysis of key targets were conducted through Metascape database. Molecular docking between identified components and core targets was conducted by TCMSP and PDB databases. amber18 software package was used to simulate 200 ns molecular dynamics of the docking complexes with the top 3 positions of bonding energy. Results A total of 22 compounds were identified, including salviaflaside, lithospermic acid, genistein, quercetin, and salvianolic acid Y, among which 16 kinds of phenolic acids and 6 kinds of flavonoids were identified. Based on the identified compounds, 502 potential anti-inflammatory targets were identified through network pharmacology. PPI analysis found that TP53, STAT3, JUN, HIF1A, CTNNB1, CASP3, and TNF were the core targets. Enrichment analysis revealed that core targets may play an anti-inflammatory role by regulating signaling pathways such as programmed death receptor 1 (PD-1), interleukin-17 (IL-17), and advanced glycation end-products/AGEs receptor (AGE-RAGE). The results of molecular docking and molecular dynamics simulation showed that the identified components were free to bind to the key targets, and the binding energy of the top 3 molecules and the receptor protein complex had relatively stable conformation, which did not lead to sustained and significant changes in conformation after binding. Conclusion Based on UHPLC-Q-TOF-MS/MS technology and network pharmacology, we can realize the basic excavation and mechanism study of anti-inflammatory substances in the stems and leaves of Prunella vulgaris, which is helpful to the development and utilization of the resources of non medicinal parts of Prunella vulgaris.

R285

湖南省教育厅重点项目（20A380）