Wang Jinghui, Zhang Meifeng, Sun Si, Wan Guoran, Wan Dong, Feng Shan, Zhu Huifeng
College of Pharmaceutical Sciences &; College of Chinese Medicine, Southwest University, Chongqing 400715, China.
Department of Clinical Medicine, Chongqing Medical University, Chongqing 400016, China.
Evid Based Complement Alternat Med. 2021 Jan 7;2021:2541316. doi: 10.1155/2021/2541316. eCollection 2021.
To apply the network pharmacology method to screen the target of catalpol prevention and treatment of stroke, and explore the pharmacological mechanism of Catalpol prevention and treatment of stroke.
PharmMapper, GeneCards, DAVID, and other databases were used to find key targets. We selected hub protein and catalpol which were screened for molecular docking verification. Based on the results of molecular docking, the ITC was used to determine the binding coefficient between the highest scoring protein and catalpol. The GEO database and ROC curve were used to evaluate the correlation between key targets.
27 key targets were obtained by mapping the predicted catalpol-related targets to the disease. Hub genes (ALB, CASP3, MAPK1 (14), MMP9, ACE, KDR, etc.) were obtained in the key target PPI network. The results of KEGG enrichment analysis showed that its signal pathway was involved in angiogenic remodeling such as VEGF, neurotrophic factors, and inflammation. The results of molecular docking showed that ACE had the highest docking score. Therefore, the ITC was used for the titration of ACE and catalpol. The results showed that catalpol had a strong binding force with ACE.
Network pharmacology combined with molecular docking predicts key genes, proteins, and signaling pathways for catalpol in treating stroke. The strong binding force between catalpol and ACE was obtained by using ITC, and the results of molecular docking were verified to lay the foundation for further research on the effect of catalpol on ACE. ROC results showed that the AUC values of the key targets are all >0.5. This article uses network pharmacology to provide a reference for a more in-depth study of catalpol's mechanism and experimental design.
应用网络药理学方法筛选梓醇防治中风的靶点，探讨梓醇防治中风的药理机制。
利用PharmMapper、GeneCards、DAVID等数据库查找关键靶点。选取筛选出的枢纽蛋白和梓醇进行分子对接验证。基于分子对接结果，采用等温滴定量热法（ITC）测定得分最高的蛋白与梓醇之间的结合系数。利用GEO数据库和ROC曲线评估关键靶点之间的相关性。
通过将预测的梓醇相关靶点映射到疾病上，获得了27个关键靶点。在关键靶点PPI网络中获得了枢纽基因（ALB、CASP3、MAPK1(14)、MMP9、ACE、KDR等）。KEGG富集分析结果表明其信号通路参与了血管生成重塑，如VEGF、神经营养因子和炎症等。分子对接结果显示ACE的对接得分最高。因此，采用ITC对ACE和梓醇进行滴定。结果表明梓醇与ACE具有较强的结合力。
网络药理学结合分子对接预测了梓醇治疗中风的关键基因、蛋白和信号通路。通过ITC获得了梓醇与ACE之间的强结合力，验证了分子对接结果，为进一步研究梓醇对ACE的作用奠定了基础。ROC结果显示关键靶点的AUC值均>0.5。本文利用网络药理学为更深入研究梓醇的作用机制和实验设计提供参考。
