藏药三果汤治疗糖尿病作用机制的网络药理学与分子对接技术分析

目的 基于网络药理学及分子对接对三果汤治疗糖尿病靶点及通路进行分析,并通过分子对接进行验证,为基础研究及临床用药提供科学依据。方法 通过中药系统药理学数据库与分子平台（TCMSP）、有机小分子生物活性数据库（PubChem）及文献查阅筛选三果汤中诃子、毛诃子、余甘子的活性成分及作用靶点,使用Genecard数据库、DRUGBANK数据库筛选糖尿病相关靶点,构建“药物-成分-靶点-疾病”网络,取药物疾病靶点交集,使用STRING构建蛋白PPI网络,运用Matescape数据库对交集靶点基因进行GO、KEGG富集分析。将筛选出的有效成分与靶点进行分子对接验证。结果 通过网络药理学收集三果汤及糖尿病靶点信息,采用GO富集分析和KEGG通路分析发现,三果汤和糖尿病共同作用靶点共269个,靶点作用最为突出的为AKT1、HAS2、MAP2、CDH1、PRKCG、PLAT,三果汤作用于糖尿病的通路主要在癌症通路、脂质和动脉粥样硬化、MAPK信号通路。分子对接结果显示,鞣花酸和没食子酸对AKT1、HAS2、MAP2存在亲和力,鞣花酸对HAS2亲和力最好。结论 通过本研究中网络药理学分析及分子对接分析,三果汤中鞣花酸、没食子酸对AKT1、HAS2、MAP2通路靶点具有亲和力,为后续实验提供研究思路。

Study on the mechanism of tibetan medicine Triphala in treating diabetes based on network pharmacology and molecular docking technology

Objective The purpose is to analyze the targets and pathways of Triphala in the treatment of diabetes based on network pharmacology and molecular docking, and to provide scientific basis for basic research and clinical medication.Methods The active components and action targets of Terminalia chebula, Terminalia chebula chebula and Phyllanthus emblica in Triphala were screened by TCMSP, PubChem and literature review. The targets related to diabetes were screened by GENECARD and DRUGBANK databases,and the "drug component target disease" network was constructed, and the intersection of drug and disease targets was selected, String was used to construct protein PPI network, and Matescape database was used to analyze go and KEGG enrichment of intersection target genes. The screened effective components were verified by molecular docking with the target. Results The target information of Triphala and diabetes was collected through network pharmacology. By go enrichment analysis and KEGG pathway analysis, it was found that there were 269 targets of Triphala and diabetes. The most prominent targets were AKT1, Has2, MAP2, CDH1, PRKCG and plat. The pathways of Triphala on diabetes were mainly cancer pathway, lipid and atherosclerosis, MAPK signal pathway. Molecular docking results showed that ellagic acid and gallic acid had affinity for AKT1, Has2 and MAP2, and ellagic acid had the best affinity for Has2. Conclusion Through the network pharmacology analysis and molecular docking analysis in this study, ellagic acid and gallic acid in Triphala have affinity for AKT1, Has2 and MAP2 pathway targets, providing research ideas for subsequent experiments.