首页|基于QSAR机器学习模型结合"久病致瘀"理论对丹参治疗慢性疼痛的分子机制研究

基于QSAR机器学习模型结合"久病致瘀"理论对丹参治疗慢性疼痛的分子机制研究

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目的:运用定量结构-活性关系(QSAR)机器学习模型和分子对接技术预测丹参治疗慢性疼痛相关的活性成分,并结合"久病致瘀"理论以及网络药理学探讨丹参治疗慢性疼痛的分子机制.方法:首先通过中药系统药理学数据库与分析平台(TCMSP)和人类基因组数据库(GeneCards)收集丹参化学成分和慢性疼痛作用靶点,筛选核心靶标,通过STRING数据库进行蛋白质相互作用(PPI)网络分析;然后对交集靶标进行基因本体(GO)富集分析和京都基因与基因组百科全书(KEGG)通路富集分析;最后运用QSAR机器学习模型以及分子对接技术筛选丹参治疗慢性疼痛的活性成分和核心靶点.结果:①筛选获得丹参与慢性疼痛的交集靶标55个、潜在活性成分55个,通过PPI分析发现丝氨酸/苏氨酸激酶1(AKT1)、表皮生长因子受体(EGFR)、白介素(IL)-6等核心靶标;②通过功能富集分析得到细胞组成45个,分子功能87个,生物过程1 450个,信号通路140条,涉及磷脂酰肌醇-3-激酶/蛋白激酶B(PI3K/AKT)、IL-17、缺氧诱导因子1(HIF-1)、环磷腺苷(cAMP)、丝裂原活化蛋白激酶(MAPK)等信号通路;③通过QSAR模型和分子对接发现丹参中的鼠尾草呋萘嵌苯、表丹参螺缩酮内脂、salvianan A、异丹参酮Ⅱ和丹酚酸C是治疗慢性疼痛的活性成分.结论:丹参治疗慢性疼痛的活性成分为二萜类化合物,其作用机制可能与调节AKT1介导的信号通路密切相关.
Study on molecular mechanism of Salviae Miltiorrhizae Radix Et Rhizoma in treatment of chronic pain based on QSAR machine learning model combined with"prolonged illness leading to stasis"theory
Objective:To predict the active components of Salviae Miltiorrhizae Radix Et Rhizoma in the treatment of chronic pain using quantitative structure-activity relationship(QSAR)machine learning model and molecular docking technology,and to explore the molecular mechanism of Salviae Miltiorrhizae Radix Et Rhizoma in the treatment of chronic pain combined with the theory of"prolonged illness leading to stasis"and network pharmacology.Methods:First,the chemical components of Salviae Miltiorrhizae Radix Et Rhizoma and targets of chronic pain were collected through the Traditional Chinese Medicine System Pharmacology Database and Analysis Platform(TCMSP)and the Human Genome database(GeneCards),and the core targets were screened.And the protein interaction(PPI)network was analyzed by STRING database.Then,the intersection targets were analyzed by Gene Ontology(GO)enrichment analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis.Finally,the QSAR machine learning model and molecular docking technology were used to screen the active ingredients and core targets of Salviae Miltiorrhizae Radix Et Rhizoma in the treatment of chronic pain.Results:①Fifty-five intersection targets and 55 potential active components of Salviae Miltiorrhizae Radix Et Rhizoma in the treatment of chronic pain were screened.The core targets such as serine/threonine kinase 1(AKT1),epidermal growth factor receptor(EGFR)and interleukin(IL)-6 were found through PPI analysis.②Through the function enrichment analysis,45 cell components,87 molecular functions,1 450 biological processes and 140 signaling pathways were obtained,involving signaling pathways such as phosphatidylinositol-3-kinase/protein kinase B(PI3K/AKT),IL-17,hypoxia-inducible factor 1(HIF-1),cyclic adenosine monophosphate(cAMP),and mitogen-activated protein kinases(MAPK)signaling pathway.③Through QSAR model and molecular docking,it was found that salvilenone,epidanshenspiroketallactone,salvianan A,isotanshinone Ⅱ and salvianolic acid C in Salviae Miltiorrhizae Radix Et Rhizoma were the active components for treating chronic pain.Conclusion:The active components of Salviae Miltiorrhizae Radix Et Rhizoma in treating chronic pain are diterpenoids,and its mechanism of action is probably related to the regulation of AKT1-mediated signaling pathways.

chronic painSalviae Miltiorrhizae Radix Et Rhizomaquantitative structure-activity relationship(QSAR)machine learningnetwork pharmacology

杨紫媛、李晨红、唐晔翎、梁鹏晨、雷璇子、常庆、马杰

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上海理工大学健康科学与工程学院(上海 200093)

上海健康医学院附属嘉定区中心医院临床科研中心(上海 201800)

上海中医药大学研究生院(上海 201203)

上海大学微电子学院(上海 201800)

上海交通大学医学院附属瑞金医院消化外科研究所(上海 200025)

上海中医药大学针灸推拿学院(上海 201203)

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慢性疼痛 丹参 定量构效关系 机器学习 网络药理学

上海市科技创新行动计划自然科学研究项目上海市卫生健康系统重要薄弱学科建设计划

21ZR14632002019ZB0203

2024

上海中医药大学学报
上海中医药大学,上海市中医药研究院

上海中医药大学学报

CSTPCD
影响因子:0.788
ISSN:1008-861X
年,卷(期):2024.38(2)
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