目的 基于超高效液相色譜-四極桿飛行時(shí)間串聯(lián)質(zhì)譜（UPLC-Q-TOF-MS/MS）、網(wǎng)絡(luò)藥理學(xué)、分子對(duì)接及分子動(dòng)力學(xué)模擬探究夏枯草莖葉總酚抗炎有效成分及其作用機(jī)制。方法 采用UPLC-Q-TOF-MS/MS技術(shù)對(duì)夏枯草莖葉水提物中成分進(jìn)行分析；使用Swiss Target Prediction、GeneCards和OMIM數(shù)據(jù)庫(kù)篩選夏枯草抗炎作用對(duì)應(yīng)的靶點(diǎn)；使用STRING數(shù)據(jù)庫(kù)和Cytoscape軟件構(gòu)建關(guān)鍵靶點(diǎn)蛋白相互作用（PPI）網(wǎng)絡(luò)；通過(guò)Metascape數(shù)據(jù)庫(kù)對(duì)關(guān)鍵靶點(diǎn)進(jìn)行基因本體論（GO）功能與京都基因與基因組百科全書（KEGG）信號(hào)通路富集分析；通過(guò)TCMSP、PDB數(shù)據(jù)庫(kù)對(duì)已鑒定的成分與核心靶點(diǎn)進(jìn)行分子對(duì)接；采用amber18軟件包，取對(duì)接結(jié)合能前3位的對(duì)接復(fù)合物分別進(jìn)行200 ns的分子動(dòng)力學(xué)模擬。結(jié)果 共鑒定出異迷迭香酸苷、紫草酸、染料木素、槲皮素和丹參酚酸Y等22個(gè)化合物，其中酚酸類16種，黃酮類6種?；阼b定出的化合物通過(guò)網(wǎng)絡(luò)藥理學(xué)得到502個(gè)潛在抗炎靶點(diǎn)，PPI分析發(fā)現(xiàn)腫瘤蛋白（TP53）、信號(hào)傳導(dǎo)和轉(zhuǎn)錄激活蛋白3（STAT3）、轉(zhuǎn)錄因子AP-1（JUN）、低氧誘導(dǎo)因子-1A（HIF1A）、黏著連接蛋白β1（CTNNB1）、半胱氨酸天冬氨酸蛋白酶-3（CASP3）、腫瘤壞死因子（TNF）等為核心靶點(diǎn)，富集分析發(fā)現(xiàn)核心靶點(diǎn)可能通過(guò)調(diào)節(jié)程序性死亡受體1（PD-1）、白細(xì)胞介素-17（IL-17）和晚期糖基化終末產(chǎn)物（AGE）/AGEs受體（RAGE）等信號(hào)通路發(fā)揮抗炎作用，分子對(duì)接與分子動(dòng)力學(xué)模擬實(shí)驗(yàn)表明已鑒定出的成分與關(guān)鍵靶點(diǎn)均能自由結(jié)合，結(jié)合能前3位的分子與受體蛋白復(fù)合物具有較穩(wěn)定構(gòu)象，結(jié)合后不會(huì)導(dǎo)致其構(gòu)象發(fā)生持續(xù)的、顯著的改變。結(jié)論 基于UHPLC-Q-TOF-MS/MS技術(shù)和網(wǎng)絡(luò)藥理學(xué)可實(shí)現(xiàn)對(duì)夏枯草莖葉抗炎物質(zhì)基礎(chǔ)挖掘和機(jī)制預(yù)測(cè)，有助于夏枯草非藥用部位資源的開發(fā)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

湖南省教育廳重點(diǎn)項(xiàng)目（20A380）