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基于三叶青抗动脉粥样硬化的网络药理学和分子对接研究
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Abstract:
[目的]本研究基于网络药理学与分子对接技术，系统探讨三叶青对动脉粥样硬化的潜在抗病机制，明确其主要有效成分、作用靶点及相关信号通路，为三叶青的药理作用研究提供理论依据。[方法]通过TCMSP数据库并结合特定筛选条件，确定三叶青的主要有效成分及其对应的靶点，并构建“有效成分–靶点网络”。进一步利用GeneCards和OMIM数据库筛选与动脉粥样硬化相关的疾病靶点，从中获取药物靶点与疾病靶点的交集。在此基础上，对交集靶点进行蛋白质互作(PPI)网络分析、基因本体(GO)生物功能富集分析和KEGG信号通路富集分析。同时应用Cytoscape 3.7.2软件中的“Analyze Network”工具，构建“有效成分–靶点–通路”网络。最后，通过分子对接方法验证关键有效成分与核心靶点之间的亲和力。[结果]筛选出三叶青的9种主要有效成分，其中7种成分与靶点具有明确的对应关系，而2种因药代动力学而舍弃。通过分析获得2084个与动脉粥样硬化相关的疾病靶点，并筛选出77个药物靶点与疾病靶点的交集。PPI网络分析显示，AKT1、PPARG、PTGS2、EGFR和ESR1等是主要的核心靶点。GO生物功能富集分析中，共获得285个与生物过程相关的条目、44个与细胞成分相关的条目以及108个与分子功能相关的条目。KEGG信号通路富集分析筛选出108条显著富集的信号通路，主要涉及内分泌抵抗、EGFR酪氨酸激酶抑制剂耐药性以及花生四烯酸代谢等关键通路。分子对接结果显示，槲皮素、山柰素、异鼠李素和山奈酚与AKT1靶点具有较高的结合亲和力，表明这些成分可能是三叶青发挥药理作用的核心成分。[结论]研究结果表明，三叶青中的核心有效成分槲皮素、山柰素、异鼠李素和山奈酚可能通过调控脂质代谢与动脉粥样硬化相关的关键信号通路，作用于核心靶点AKT1，从而在蛋白质结合、酶结合及可识别蛋白结合等生物学功能中发挥抗动脉粥样硬化的作用。本研究为三叶青的进一步开发和利用提供了理论支持。
Aim: This study systematically investigates the potential anti-atherosclerotic mechanisms of Tetrastigma hemsleyanum (San Ye Qing) using network pharmacology and molecular docking approaches. It aims to identify the primary active components, potential targets, and associated signaling pathways, providing a theoretical foundation for the pharmacological research of Tetrastigma hemsleyanum. Methods: The main active components of Tetrastigma hemsleyanum and their corresponding targets were identified through the TCMSP database with specific screening criteria, and the “active component-target network” was constructed. Subsequently, disease targets related to atherosclerosis were retrieved from the GeneCards and OMIM databases, and the intersection of drug targets and disease targets was obtained. Protein-protein interaction (PPI) network analysis, Gene Ontology (GO) biological function enrichment analysis, and KEGG pathway enrichment analysis were performed on the intersected targets. The “active component-target-pathway network” was constructed using the Analyze Network tool in Cytoscape 3.7.2. Finally, molecular docking was employed to verify the binding affinity between key active components and core targets. Results: Nine major active components of Tetrastigma hemsleyanum were identified, seven of which had clear corresponding targets, while two were excluded due to poor pharmacokinetic properties. A total of 2084 disease-related targets for atherosclerosis
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