蛋白质组学技术在网络药理学中的应用

蛋白质组学发展至今已日趋成熟,在生物医药相关领域研究中的应用显著增加,与之相关的样品制备技术、蛋白定量方法及先进的质谱仪器也得到了快速发展。网络药理学是近年来提出的新药发现新策略,是药理学的新兴分支学科,它从整体的角度探索药物与疾病的关联性,发现药物靶标,指导新药研发。将蛋白质组学技术应用于网络药理学研究,能使研究人员系统地预测和解释药物的作用,加速药物靶点的确认,从而设计多靶点药物或药物组合。综述了蛋白质组学技术的新近研究进展,并简单概述了其在网络药理学中的应用。     

中药网络药理学研究中的生物信息学方法

为了更有效地治疗癌症、心血管疾病、免疫系统疾病等复杂疾病,基于分子网络的多靶点药物发现理念逐渐成为一种新的趋势,而中药整体、辨证、协同的用药观再一次引起了药物发现领域的极大兴趣。中药在治疗复杂慢性疾病方面有确切的疗效和较小的毒副作用。中药网络药理学从分子网络调控的水平上阐明中药的作用机制,为多靶点药物发现提供有益的启示和借鉴,并有可能从临床有效的中药反向开发现代多组分、多靶点新药。针对基于生物分子网络的中药药理学研究路线中的4 个步骤,介绍近年来中药网络药理学研究中相关的生物信息学方法。     

甘草抗动脉粥样硬化机制的网络药理学研究

利用网络药理学方法探讨甘草在抗动脉粥样硬化中的分子机制。本研究利用中医药系统药理学数据库和分析平台(traditional Chinese medicine systems pharmacology database and analysis platform,TCMSP)分析甘草中的有效活性成分,并获得有效成分的作用靶点。通过Cytoscape软件构建可视化靶点互相作用网络,对网络中的关键靶点进行基因本体(GO)富集分析和KEGG通路富集分析。结果显示甘草中40种有效活性成分的预测靶点共97个,47个靶点与动脉粥样硬化(AS)相关,其中18个是血管保护药物和脂质修饰药物的作用靶点,表明甘草可作为调控AS发展的药物。基于97个预测靶点的GO富集分析,发现甘草可参与多种生物学过程,尤其是应对外源性刺激,以及参与细胞凋亡等过程。通过构建甘草靶点与AS疾病靶点相互作用网络(PPI),确定了AKT1、MAPK3、MAPK1、JUN和CASP3等关键靶点,并对关键靶点进行KEGG富集分析,结果表明甘草主要影响调控细胞增殖、生存以及凋亡的细胞信号转导相关通路,并激活先天免疫相关信号通路,调节炎性细胞因子释放,从而发挥抗动脉粥样硬化作用。甘草具有多成分、多靶点、多途径的作用特点,主要通过PI3K-AKT信号途径、MAPK信号途径、NOD样受体信号通路调控细胞增殖和凋亡,同时发挥免疫调控作用,从而影响动脉粥样硬化的发展,由此可见,甘草可作为动脉粥样硬化疾病治疗的候选中草药。     

基于网络药理学和分子对接探讨苓桂术甘汤治疗冠心病的作用机制

苓桂术甘汤是出自伤寒论的经典名方，临床上对治疗冠心病(coronary heart disease,CHD)具有显著疗效。由于苓桂术甘汤复杂的分子组成和多样的药学活性，且关于其治疗CHD的基础理论研究较少，目前苓桂术甘汤治疗CHD的作用机制和靶点尚未完全阐明。本文采用网络药理学的研究方法探索苓桂术甘汤治疗CHD的作用机制，并利用分子对接验证药物效应分子与靶点间的相互作用。本文通过TCMSP数据库获取苓桂术甘汤中103个有相关靶点信息的活性成分及对应靶点，通过GeneCards数据库和DisGeNET数据库获取4 618个CHD相关靶点蛋白。将苓桂术甘汤与CHD的交集靶点导入到Cytoscape3.9.1软件中构建“苓桂术甘汤-活性成分-CHD作用靶点”网络，并利用STRING数据库获取交集靶点相互作用关系，筛选出AKT1、TP53、STAT3等核心靶点并绘制PPI网络。利用交集靶点在David数据库中进行GO和KEGG富集分析发现，苓桂术甘汤通过调控细胞凋亡、细胞增殖、血管生成、胆固醇代谢、炎症反应以及对脂多糖、缺氧、肿瘤坏死因子的响应等生物学过程和AGE-RAGE、脂质与动脉粥样硬化...     

基于网络药理学探讨“黄芪-白术-熟地黄”组方防治肾病综合征的作用机制

本研究旨在通过网络药理学方法和分子对接技术探讨黄芪-白术-熟地黄组方(HBS)治疗肾病综合征的作用机制.通过多个数据库获取肾病综合征基因并进行功能模块分解,找出肾病综合征基因参与的主要生物学过程.通过文献以及数据库查找HBS活性成分和基因靶点,筛选出HBS治疗肾病综合征的有效靶点.通过有效靶点的KEGG和GO富集分析,...     

基于网络药理学的大黄治疗阿尔茨海默病作用机制研究

基于网络药理学探讨大黄治疗阿尔茨海默病(AD)的作用机制.借助TCMSP数据库及Uniprot数据库筛选出大黄有效成分及靶点基因.通过Drugbank、Dis Ge NET和TTD数据库筛选出阿尔茨海默病的靶点基因;成分靶点与疾病靶点映射后使用Cytoscape 3.7.1软件构建药物有效成分-靶点蛋白相互作用网络,使...     

基于网络药理学的四君子汤治疗阿尔茨海默病作用机制研究

基于网络药理学探讨四君子汤治疗阿尔茨海默病(AD)的作用机制.借助TCMSP数据库及Uniprot数据库筛选出四君子汤有效成分及靶点基因.通过Drugbank、Dis Ge NET和TTD数据库筛选出阿尔茨海默病的靶点基因;成分靶点与疾病靶点映射后使用Cytoscape 3.7.1软件构建药物有效成分-靶点蛋白相互作用...     

基于网络药理学探讨丹参-丹皮配伍抗脑缺血损伤的作用机制

本文旨在通过网络药理学和分子对接方法探讨丹参-丹皮活性成分治疗脑卒中的潜在分子机制.首先基于中药系统药理学分析平台筛选丹参、丹皮的活性成分及其作用靶点,利用CTD、TTD和GeneCards数据库收集脑卒中相关靶点.然后将药物和疾病靶点取交集,借助STRING数据库获取靶点间相互作用关系,利用R语言的Cluster-P...     

基于蛋白互作和关键作用靶点的网络药理学研究当归四逆汤治疗腰椎间盘突出症的作用机理

基于网络药理学探求当归四逆汤作用于腰椎间盘突出症的作用机理.系统检索中药系统药理学技术平台(TCMSP)、中药潜在靶点数据库(TCM-PTD)获取当归四逆汤药物的有效活性成分45个,作用靶点132个,并将得到的靶点与通用蛋白数据库(Uniprot)中载入的基因名称简称进行匹配.通过检索人类基因数据库(GeneCards...     

基于网络药理学和分子对接探索桑白皮治疗糖尿病周围神经病变的潜在机制

本研究运用网络药理学和分子对接方法对中药桑白皮治疗糖尿病周围神经病变(DPN)的活性成分、潜在作用靶点和信号通路进行研究,探索桑白皮治疗DPN的可能作用机制。首先从中药系统药理学数据库(TCMSP)筛选出桑白皮的活性成分及靶点基因。通过GeneCards数据库及OMIM数据库筛选出DPN的疾病靶点基因,并用Cytoscape软件构建"药物-有效成分-靶基因-疾病"中药调控网络图。将有效成分靶标与疾病靶标上传到STRING数据库,构建蛋白互作网络图(PPI),并使用R语言对得到的PPI进行核心基因的筛选。运用R语言对关键靶点进行GO富集分析和KEGG通路富集分析。其次从活性成分及靶点基因中根据degree值筛选出前3个关键成分,并将该网络中的基因靶点以degree值高低进行排序,选择前3个核心靶点,然后从RCSB数据库下载相关蛋白的结构,使用Pymol软件去除溶剂分子与配体,使用AutoDock软件进行分子对接。最后通过酶联免疫吸附实验和荧光光谱实验验证网络药理学富集分析的结果。最终预测到31个桑白皮活性成分,312个活性成分相关靶点,120个桑白皮-糖尿病周围神经病变共同有效靶点。活性...     

Network pharmacology and drug discovery

The pharmaceutical industry has historically relied on particular families of 'druggable' proteins against which to develop compounds with desired actions. But proteins rarely function in isolation in and outside the cell; rather, proteins operate as part of highly interconnected cellular networks. Network pharmacology is an emerging area of pharmacology which utilizes network analysis of drug action. By considering drug actions in the context of the cellular networks, network analysis promises to greatly increase our knowledge of the mechanisms underlying the multiple actions of drugs. Network pharmacology can provide new approaches for drug discovery for complex diseases. This review introduced the recent progress of network pharmacology and its importance to understand the mechanism of drug actions and drug discovery.     

Applications of flow cytometry in cellular pharmacology

Cellular pharmacology is defined as the study of drug effects on various cell functions. Flow cytometry enriches cellular pharmacology by the following possibilities for efficient analysis. Firstly, the determination of toxic concentrations can be approached by the assessment of cell viability. However, due to the existence of many fluorescent DNA probes, most studies are devoted to the investigation of products acting on cell division, particularly in the area of antineoplastic drugs. The effects of drugs on respiration can be approached by analysis of mitochondrial activities. On the other hand, the studies of drug actions on cell differentiation functions have been started using antisera or monoclonal antibodies to cell-specific proteins such as collagen and keratin. Flow cytometry appears to be more and more important in the progress of cellular toxicology and pharmacology.     

A survey of web resources and tools for the study of TCM network pharmacology

Background: Traditional Chinese medicine (TCM) treats diseases in a holistic manner, while TCM formulae are multi-component, multi-target agents at the molecular level. Thus there are many parallels between the key ideas of TCM pharmacology and network pharmacology. These years, TCM network pharmacology has developed as an interdisciplinary of TCM science and network pharmacology, which studies the mechanism of TCM at the molecular level and in the context of biological networks. It provides a new research paradigm that can use modern biomedical science to interpret the mechanism of TCM, which is promising to accelerate the modernization and internationalization of TCM. Results: In this paper we introduce state-of-the-art free data sources, web servers and softwares that can be used in the TCM network pharmacology, including databases of TCM, drug targets and diseases, web servers for the prediction of drug targets, and tools for network and functional analysis. Conclusions: This review could help experimental pharmacologists make better use of the existing data and methods in their study of TCM.     

Predicting new molecular targets for rhein using network pharmacology

Background

Drugs can influence the whole biological system by targeting interaction reactions. The existence of interactions between drugs and network reactions suggests a potential way to discover targets. The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of drug-targets in current datasets are validated interactions. This motivates the need for developing computational methods that predict true interaction pairs with high accuracy. Currently, network pharmacology has used in identifying potential drug targets to predicting the spread of drug activity and greatly contributed toward the analysis of biological systems on a much larger scale than ever before.

Methods

In this article, we present a computational method to predict targets for rhein by exploring drug-reaction interactions. We have implemented a computational platform that integrates pathway, protein-protein interaction, differentially expressed genome and literature mining data to result in comprehensive networks for drug-target interaction. We used Cytoscape software for prediction rhein-target interactions, to facilitate the drug discovery pipeline.

Results

Results showed that 3 differentially expressed genes confirmed by Cytoscape as the central nodes of the complicated interaction network (99 nodes, 153 edges). Of note, we further observed that the identified targets were found to encompass a variety of biological processes related to immunity, cellular apoptosis, transport, signal transduction, cell growth and proliferation and metabolism.

Conclusions

Our findings demonstrate that network pharmacology can not only speed the wide identification of drug targets but also find new applications for the existing drugs. It also implies the significant contribution of network pharmacology to predict drug targets.     

Choke point analysis of metabolic pathways in E.histolytica: a computational approach for drug target identification

With the Entamoeba genome essentially complete, the organism can be studied from a whole genome standpoint. The understanding of cellular mechanisms and interactions between cellular components is instrumental to the development of new effective drugs and vaccines. Metabolic pathway analysis is becoming increasingly important for assessing inherent network properties in reconstructed biochemical reaction networks. Metabolic pathways illustrate how proteins work in concert to produce cellular compounds or to transmit information at different levels. Identification of drug targets in E. histolytica through metabolic pathway analysis promises to be a novel approach in this direction. This article focuses on the identification of drug targets by subjecting the Entamoeba genome to BLAST with the e-value inclusion threshold set to 0.005 and choke point analysis. A total of 86.9 percent of proposed drug targets with biological evidence are chokepoint reactions in Entamoeba genome database.     

Dynamical analysis of cellular networks based on the Green's function matrix

The complexity of cellular networks often limits human intuition in understanding functional regulations in a cell from static network diagrams. To this end, mathematical models of ordinary differential equations (ODEs) have commonly been used to simulate dynamical behavior of cellular networks, to which a quantitative model analysis can be applied in order to gain biological insights. In this paper, we introduce a dynamical analysis based on the use of Green's function matrix (GFM) as sensitivity coefficients with respect to initial concentrations. In contrast to the classical (parametric) sensitivity analysis, the GFM analysis gives a dynamical, molecule-by-molecule insight on how system behavior is accomplished and complementarily how (impulse) signal propagates through the network. The knowledge gained will have application from model reduction and validation to drug discovery research in identifying potential drug targets, studying drug efficacy and specificity, and optimizing drug dosing and timing. The efficacy of the method is demonstrated through applications to common network motifs and a Fas-induced programmed cell death model in Jurkat T cell line.     

Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders

In a research environment dominated by reductionist approaches to brain disease mechanisms, gene network analysis provides a complementary framework in which to tackle the complex dysregulations that occur in neuropsychiatric and other neurological disorders. Gene–gene expression correlations are a common source of molecular networks because they can be extracted from high‐dimensional disease data and encapsulate the activity of multiple regulatory systems. However, the analysis of gene coexpression patterns is often treated as a mechanistic black box, in which looming ‘hub genes’ direct cellular networks, and where other features are obscured. By examining the biophysical bases of coexpression and gene regulatory changes that occur in disease, recent studies suggest it is possible to use coexpression networks as a multi‐omic screening procedure to generate novel hypotheses for disease mechanisms. Because technical processing steps can affect the outcome and interpretation of coexpression networks, we examine the assumptions and alternatives to common patterns of coexpression analysis and discuss additional topics such as acceptable datasets for coexpression analysis, the robust identification of modules, disease‐related prioritization of genes and molecular systems and network meta‐analysis. To accelerate coexpression research beyond modules and hubs, we highlight some emerging directions for coexpression network research that are especially relevant to complex brain disease, including the centrality–lethality relationship, integration with machine learning approaches and network pharmacology .