吉林大学学报(医学版) ›› 2023, Vol. 49 ›› Issue (1): 74-83.doi: 10.13481/j.1671-587X.20230110

• 基础研究 • 上一篇    下一篇

基于锦灯笼对白血病作用机制的网络药理学和分子对接技术的生物信息学分析

汪洺卉,刘墨祎,王鹤霖,李迎,汪香君,惠赫童,范馨元,王添琦,刘丽梅()   

  1. 北华大学医学技术学院微生物学检验教研室,吉林 吉林 132000
  • 收稿日期:2022-04-07 出版日期:2023-01-28 发布日期:2023-02-08
  • 通讯作者: 刘丽梅 E-mail:Liulm74@163.com
  • 作者简介:汪洺卉(1997-),女,吉林省吉林市人,在读硕士研究生,主要从事白血病发病机制方面的研究。
  • 基金资助:
    吉林省科技厅科技发展计划项目(20200403118SF)

Bioinformatics analysis of network pharmacology and molecular docking technology based on mechanism of Physalis Calyx seu Fructus on leukemia

Minghui WANG,Moyi LIU,Helin WANG,Ying LI,Xiangjun WANG,Hetong HUI,Xinyuan FAN,Tianqi WANG,Limei LIU()   

  1. Department of Microbiology Laboratory,School of Medical Technology,Beihua University,Jilin 132000,China
  • Received:2022-04-07 Online:2023-01-28 Published:2023-02-08
  • Contact: Limei LIU E-mail:Liulm74@163.com

摘要:

目的 基于网络药理学探讨锦灯笼(PCF)在白血病发生发展过程的作用,阐明其生物活性成分主要作用靶点和信号通路相关机制。 方法 利用中药系统药理学数据库与分析平台(TCMSP)数据库以“锦灯笼”为关键词检索其生物活性成分及对应的潜在靶点,并采用蛋白质数据库(PDB)和小分子药物靶点预测在线平台(Swiss Target Prediction)网站对其潜在靶点进行评估预测。采用GeneCards、OMIM和DrugBank数据库以“Leukemia”为关键词对相关靶点进行检索。利用Draw Veen Diagram在线软件将PCF生物活性成分靶点与白血病靶点交集,得到的交集靶点即为PCF作用于白血病的靶点;利用基因/蛋白相互作用检索搜查工具(STRING)数据库获取交集靶点蛋白的蛋白-蛋白互作(PPI)网络信息并进行可视化处理;采用注释可视化和集成发现数据库(DAVID)对交集基因进行基因本体(GO)生物功能富集分析和京都基因与基因组百科全书(KEGG)信号通路富集分析,并利用Cytoscape 3.7.2软件对PPI网络进行拓扑分析筛选核心靶点和核心活性成分,采用 AutoDock 和PyMol软件进行分子对接验证。 结果 对TCMSP数据库检索结果进行筛选后得到6个PCF活性成分,收集PCF与白血病的共同靶点29个。GO富集分析主要涉及造血或淋巴器官的发育、蛋白代谢调控和细胞凋亡等生物进程;KEGG信号通路富集分析主要包括癌症通路、肿瘤坏死因子 (TNF)和T细胞受体 (TCR) 等信号通路。拓扑网络分析得到蛋白激酶B1 (AKT1)、含半胱氨酸的天冬氨酸蛋白水解酶3 (Caspase-3) 等10个核心靶点,结合KEGG信号通路筛选出核心活性成分谷甾醇和禾本甾醇。分子对接,PCF活性成分谷甾醇与原癌基因 JUN及禾本甾醇与AKT1和Caspase-3对接能均<-6.0 kcal·mol-1,表明对接较好。 结论 PCF的主要活性成分谷甾醇和禾本甾醇可能通过调控JUN、AKT1和Caspase-3等基因参与癌症通路和TCR等信号通路,进而影响白血病的发生发展。

关键词: 网络药理学, 分子对接技术, 锦灯笼, 白血病

Abstract:

Objective To explore the effect of Physalis Calyx seu Fructus (PCF) on the occurrence and development of leukemia based on network pharmacology, and to clarify the main targets and signaling pathway-related mechanism of its bioactive components. Methods Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database was used to search its bioactive components and corresponding potential targets with “Physalis Calyx seu Fructus” as the keyword; the potential targets were evaluated and predicted with Protein Data Bank (PDB) and Swiss Target Prediction website; GeneCards database, Online Mendelian Inheritance in Man (OMIM) database and DrugBank database were used to search the relevant targets with “leukemia” as the keyword. The bioactive component targets of PCF and leukemia target were intersected by Draw Veen Diagram online software, and the intersected targets were the targets of PCF acting on leukemia; the network information of protein-protein interaction (PPI) of intersection target proteins was obtained and visualized with STRING database; DAVID database was used for Gene Ontology (GO) biological function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analysis of intersection genes; topological analysis on PPI network was performed with Cytoscape 3 7.2 to screen the core targets and the core active components; the molecular docking verification was performed with AutoDock and PyMol. Results After screening the retrieval results of TCMSP database, 6 active components of PCF were obtained; 29 common targets of PCF and leukemia were collected; GO enrichment analysis mainly involved the development of hematopoietic and lymphoid organs, protein metabolism regulation, apoptosis and other biological processes; KEGG enrichment analysis mainly included cancer pathway, tumor necrosis factor (TNF), T cell receptor (TCR) and other signal pathways. Ten core targets such as protein kinase B1 (AKT1) and cysteinyl aspartate specific proteinase-3(Caspase-3) were obtained by topological network analysis, combined with KEGG signaling pathway, the core active components Sitosterol and Gramisterol were obtained. The results of molecular docking showed that the docking energy of Sitosterol with proto-oncogene (JUN), Gramisterol with AKT1 and Caspase-3 were -6.0 kcal·mol-1,indicating the docking was good. Conclusion The main active components of PCF, such as Sitosterol and Gramisterol, may affect the occurrence and development of leukemia by regulating JUN, AKT1, Caspase-3 and other genes and participating in cancer pathway, TCR and other signaling pathways.

Key words: Network pharmacology, Molecular docking technology, Physalis Calyx seu Fructus, Leukemia

中图分类号: 

  • R733.7