Mechanism of Smilax china L. in the treatment of hepatic fibrosis based on network pharmacology and molecular docking technology

Liqin Chao, Junxia Zhang, Fengping Sun
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Abstract

Objective: To investigate the mechanism of the active ingredients of Smilax china L. in the treatment of hepatic fibrosis (HF). Methods: The targets of the active ingredients of Smilax china L. were predicted using the TCMSP database, PubChem database, Babel software, Swiss Target Prediction platform, SEA platform, and UniProt database. The targets for HF were obtained using the DisGeNET database. Venn diagram platforms were used to intersect the active ingredients of Smilax china L. and HF targets. The key targets were selected using the STRING database to construct a protein-protein interaction network model. The key compounds were selected using the Cytoscape software to construct an active Smilax china L. ingredient-action target network. The intersection target was used for GO enrichment analysis and KEGG metabolic pathway analysis by ClueGO. The DockThor program was used to connect the important active ingredients and compounds of Smilax china L. with the corresponding intersection targets by calculating the binding energy and using the PyMOL software to create visual images. Results: A total of 9 active ingredients, 209 targets, 56 targets, and 15 key targets related to HF were identified from Smilax china L. The biological processes associated with Smilax china L. for preventing HF mainly included collagen metabolism, positive regulation of vascular endothelial cell migration, muscle cell proliferation, and smooth muscle cell proliferation. The pathways mainly included the IL-17 signaling pathway, estrogen signaling pathway, prolactin signaling pathway, and pancreatic cancer pathway. Conclusion: The mechanism of Smilax china L. in the treatment of HF was preliminarily explored. Smilax china L. has multi-component and multi-target characteristics, through which it can be used to treat HF.
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基于网络药理学和分子对接技术的菝葜治疗肝纤维化机制研究
目的:探讨菝葜有效成分治疗肝纤维化(HF)的作用机制。方法:利用TCMSP数据库、PubChem数据库、Babel软件、Swiss Target Prediction平台、SEA平台和UniProt数据库对菝葜有效成分的靶点进行预测。利用DisGeNET数据库获得HF靶蛋白。采用维恩图平台将菝葜有效成分与HF靶标相交。利用STRING数据库选择关键靶点,构建蛋白-蛋白相互作用网络模型。利用Cytoscape软件筛选关键化合物,构建菝葜活性成分-作用靶点网络。交叉靶点通过ClueGO进行GO富集分析和KEGG代谢途径分析。利用DockThor程序通过计算结合能,将菝葜中重要的有效成分和化合物与相应的交叉靶点连接起来,并利用PyMOL软件生成可视化图像。结果:从菝葜中共鉴定出9种与HF相关的有效成分、209个靶点、56个靶点和15个关键靶点。菝葜预防HF的生物学过程主要包括胶原代谢、正向调节血管内皮细胞迁移、肌肉细胞增殖和平滑肌细胞增殖。途径主要包括IL-17信号通路、雌激素信号通路、催乳素信号通路和胰腺癌信号通路。结论:初步探讨了菝葜治疗心衰的作用机制。菝葜具有多成分、多靶点的特点,可用于治疗心衰。
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