化学蛋白质组学与药物靶点的发现

小分子药物靶点的发现对于生物和医学的研究者而言,是一项既重要又艰巨的任务,医学和药学界研究工作者急切需要发现和确认新的靶点。为了克服药物靶点确认的瓶颈,已经发展了许多新技术用以研究小分子化合物与蛋白质分子间的相互作用,其中包括化学蛋白质组学方法。化学蛋白质组是全蛋白质组学研究的一个亚类,化学蛋白质组学是利用能够与靶蛋白质发生特异性相互作用的化学小分子来干扰和探测蛋白质组,在分子水平上系统揭示特定蛋白质的功能以及蛋白质与化学小分子的相互作用,从而准确找到药物作用靶点的组学研究方法。化学蛋白质组学技术和方法不断成熟,在药物作用靶点的发现、确认和药物多靶点研究等方面都将起到重要的作用,并将大大提高药物发现的效率。     

活性蛋白质组技术应用于中药活性分子靶点的研究进展

活性蛋白质组技术（ABPP）是越来越多的化学蛋白质组学方法之一,该技术利用小分子化学探针来探索化合物和靶点之间的相互作用机制。在中药活性成分分子靶点的鉴定中,ABPP技术具有高效、准确等优越性,目前已经利用该技术鉴定了许多中药活性成分的分子靶点。综述了ABPP技术在中药活性分子靶点中的应用,包括青蒿素、黄芩苷、穿心莲内酯、苏木酮、龙牙葱木皂苷、山楂酸、蟾毒内酯、毛兰素与荜茇宁等,为创新药物靶点的发现提供新的思路。     

基于核糖体结构和功能的新型抗生素研究进展

本文介绍了以核糖体为靶点的几种重要抗生素的作用机制,在此基础上对近几年来出现的此类药物研究的新方法新思路进行了综述。远程交互作用网络及诱导-契合机制的发现、抗生素抑制蛋白质合成的新靶点的发掘、利用计算机辅助药物设计技术及其他物理化学分析方法如分子建模技术、计算机动态模拟技术、分子对接技术、X-ray衍射技术进行新型抗生素研究,使我们对于药物与核糖体的结合方式及结合位点有了更加深入的了解,为科研人员对新型抗生素的开发和研究带来了无限的希望。     

基于全球上市药物数据的抗阿尔茨海默病重定位新药发现

目的从上市药物中寻找潜在的多靶点抗阿尔茨海默病(AD)药物。方法本文利用实验室前期基于机器学习算法建立的抗AD多靶点药物预测平台,对全球上市药物数据库进行了预测和挖掘。结果从全球上市药物数据库中预测出13个至少作用于1个抗AD药物靶点,且可以通过多种作用对抗AD的上市药物,利用Cytoscape构建化合物——靶点网络;并针对上市药物阿戈美拉汀及其预测的多靶点蛋白进行分子对接以验证预测结果,蛋白靶点包括ADORA2A,ACHE,BACE1,PTGS2,MAOB,SIGMAR1和ESR1,阿戈美拉汀均能与以上靶点产生相互作用。结论本研究应用机器学习算法、网络药理学方法及分子对接方法,初步揭示了上市药物数据库中抗AD作用的潜在药物,为上市药物抗AD作用重定位提供了重要信息。     

基于数据库的大黄素协同化疗药物抗肿瘤机制探讨

目的 通过药物靶点数据库对大黄素分子机制进行模拟探讨,为研究大黄素协同抗肿瘤化疗药物提供新的研究策略.方法 借助在线数据库资源收集大黄素已知靶点,通过文献及靶点功能数据库筛选具有协同肿瘤化疗药物的潜在靶点,根据靶点构建蛋白-蛋白相互作用(PPI)网络,对潜在靶点KEGG通路分析,明确其发挥协同作用分子机制.结果 大黄素...     

基于网络药理学研究花贝消痤汤治疗痤疮的作用机制

目的 基于网络药理学及分子对接技术探索花贝消痤疮汤治疗痤疮的分子机制。方法 通过TCMSP及ETCM数据库筛选花贝消痤汤主要活性成分并通过SwissTargetPrediction网站进行靶点预测;同时从Gene Card数据库、Disgent数据库、TTD数据库筛选痤疮的相关靶点;成分靶点与疾病靶点映射后使用String网站及Cytoscape 3.9.1软件构建蛋白质-蛋白质相互作用网络;通过Metascape数据库进行GO与KEGG分析;Cytoscape 3.9.1软件构建药物-成分-靶点-通路网络图;采用分子对接技术对潜在活性成分与关键靶点进行匹配。结果 从花贝消痤汤中筛选出207个活性成分和1 722个潜在靶点,其中70个核心靶点20条通路与其治疗痤疮有密切关系,分子对接结果显示药物有效成分与其关键靶点都具有良好的结合活性。结论 花贝消痤汤治疗痤疮具有生物学支撑。不同的蛋白存在协同作用,其中SRC、MAPK1、STAT3、RELA、RXRA、EP300、NFKB1、NFKBIA等靶点起着关键作用。生物过程主要涉及激素合成、内分泌调节、血管生成、细胞信号传导、炎症反应等方面,...     

基于网络药理学与分子对接技术研究刺五加总苷改善睡眠的药理机制

目的 通过网络药理学与分子对接技术探讨刺五加苷改善睡眠的药理机制。方法 通过文献归纳刺五加总苷的化学成分，借助GeneCards数据库预测刺五加苷以及失眠的相关靶点；使用Cytoscape软件构建成分-靶点图；利用Metascape对相关靶点进行GO和KEGG富集分析。使用AutoDock和PyMOL软件对成分与核心靶点进行分子对接验证。结果 刺五加总苷中共筛选49种已知结构的化合物成分以及其对应的168个靶点，67个靶点与改善睡眠相关。核心靶点主要有TNF、GAPDH、VEGFA等，涉及的主要信号通路有神经活性配体-受体相互作用、血清素突触、钙离子信号通路等；分子对接结果表明核心成分与关键靶点均可自发结合，并且有较高的结合活性。结论 刺五加苷类成分与TNF、GAPDH、VEGFA等靶点相互作用，通过血清素突触、钙离子信号通路、PI3K-Akt等多个信号通路参与炎症、神经内分泌调节等发挥改善睡眠的作用。     

基于网络药理学和分子对接探究柴胡清肝汤治疗肉芽肿性乳腺炎的作用机制

目的 基于网络药理学和分子对接技术探讨柴胡清肝汤治疗肉芽肿性乳腺炎的作用机制。方法 利用TCMSP数据库和Uniprot数据库筛选柴胡清肝汤的有效活性成分和作用靶点信息,利用GeneCards数据库获取肉芽肿性乳腺炎的相关靶点,并利用Cytoscape 3.9.1软件构建药物-成分-共同靶点关系网络。通过Venny 2.1.0构建韦恩图获取药物和疾病的共同靶点,利用STRING数据库获取核心蛋白的相互作用网络,运用DAVID数据库对关键靶点进行GO富集和KEGG通路富集分析,运用AutoDock及Pymol对关键靶点与相应活性成分进行分子对接验证,分析柴胡清肝汤治疗肉芽肿性乳腺炎的潜在作用机制。结果 共获得柴胡清肝汤有效活性成分199个,药物-疾病作用靶点23个,其中槲皮素、山柰酚、β-谷甾醇、豆甾醇、黄芩素等关键有效活性成分与IL-6、TNF、ALB、IL-1β、CXCL8、STAT3、CCL2、IL-4等靶点相互作用,调控IL-17、Toll样受体、细胞因子受体相互作用、NF-κB、TNF等信号通路,分子对接结果显示筛选的主要活性成分与其对应靶蛋白具有较好的结合活性。结论 柴胡清肝...     

基于网络药理学方法研究知母治疗2型糖尿病作用机制

目的 使用网络药理学技术探讨知母治疗2型糖尿病（T2DM）作用机制。方法 挖掘中药成分数据库中知母化学成分,获取化学成分的药代动力学参数用于构建药物的化学成分数据库;利用ADME模型筛选潜在活性分子;基于随机森林和支持向量机的方法预测活性分子作用靶点并构建知母化合物-靶点（C-T）网络;从多个人类疾病数据库检索T2DM相关靶点并构建疾病蛋白-蛋白相互作用（PPI）网络,构建知母活性-疾病靶点（C-D）网络;通过富集分析构建知母活性分子作用靶点-通路（CD-M）网络。结果 该研究得到的知母化学成分数据库一共有81个分子,通过ADME模型筛选得到15个活性分子,通过分析化合物作用靶点网络发现知母活性分子作用20个T2DM靶点蛋白,富集分析发现知母活性分子通过调控胰岛素通路、胰岛素抵抗通路、PI3K-Akt信号、NOS3通路、HIF-1通路、能量代谢等途径治疗T2DM。结论 知母可通过多成分、多靶点、多途径的协同作用治疗T2DM。     

多靶点鬼臼毒素新衍生物CCP-1抗肿瘤多药耐药作用机制研究

<正>以往单一靶点的抗肿瘤药物极易产生耐药[1-3]。多靶点的化疗药物具有其独特优势,如多靶点的化疗药物是单分子化合物,不存在多药物联合应用中的药物相互作用问题,也可避免药物联合应用中药物配伍、剂量、给药方式等问题。本课题组以天然产物中鬼臼毒素为母核,整合了具有抗肿瘤耐药的小分子片段,将鬼臼毒素新衍生物设计为同时作用于多个耐药靶点的化合物,以更好的克服传统单靶点化疗药物     

Modeling of PET data in CNS drug discovery and development

Positron emission tomography (PET) is increasingly used in drug discovery and development for evaluation of CNS drug disposition and for studies of disease biomarkers to monitor drug effects on brain pathology. The quantitative analysis of PET data is based on kinetic modeling of radioactivity concentrations in plasma and brain tissue compartments. A number of quantitative methods of analysis have been developed that allow the determination of parameters describing drug pharmacokinetics and interaction with target binding sites in the brain. The optimal method of quantification depends on the properties of the radiolabeled drug or radioligand and the binding site studied. We here review the most frequently used methods for quantification of PET data in relation to CNS drug discovery and development. The utility of PET kinetic modeling in the development of novel CNS drugs is illustrated by examples from studies of the brain kinetic properties of radiolabeled drug molecules.     

Drug affinity responsive target stability and its application: Review北大核心CSCD

A drug target refers to specific molecules that exist within tissue cells and interact with a drug to produce the drug effect. More than 98% of drug targets belong to protein. The interactions between a drug and the target in cells play a key role in the drug effect. How to use various methods to find and confirm the new target of a drug is one of the important challenges facing researchers. Drug affinity responsive target stability (DARTS) is a new technique based on the principle that when a small molecule compound binds to a protein, the interaction stabilizes the target protein's structure so that it becomes protease resistant. This technique is universally applicable to drug screening and target identification because it requires no modification of the drug and is independent of the mechanism of drug action. This paper reviews the discovery of DARTS method, technical principles, methodology and its applications in researches.     

基于药物靶标识别中药活性成分的研究方法及应用

中药小分子与生物体内靶标蛋白的相互作用是中药发挥药理作用的基础，现代科学技术的发展和药物作用靶标的揭示为中药活性成分的研究提供了新的技术和手段。基于已知的药物作用靶标，阐明中药的有效成分及其作用机制已成为中药学研究和发展的重要方向。基于化学和生物技术与计算机虚拟筛选技术两大视角，总结了亲和超滤质谱技术、分子生物色谱技术、磁珠富集技术、等离子共振技术、生物膜干涉技术、分子对接技术、药效团模型和机器学习8种基于药物靶标识别中药活性成分的研究方法与应用现状，以期补充传统药物发现的方法，为该领域研究提供借鉴与参考。     

Computational approaches in chemogenomics and chemical biology: current and future impact on drug discovery

Background: Chemical biology and chemogenomics are rapidly evolving disciplines at interfaces between chemistry and the life sciences and are highly interdisciplinary in nature. Chemogenomics has a strong conceptional link to modern drug discovery research, whereas chemical biology focuses more on the use of small molecules as probes for exploring biological functions, rather than drug candidates. However, the boundaries between these areas are fluid, as they should be, given their strong interdisciplinary orientation. Objective: Recently, computational approaches have been introduced for the analysis of research topics that are of considerable relevance for these disciplines including, for example, the systematic study of ligand–target interactions or mapping of pharmacologically relevant chemical space. This contribution introduces key investigations in computational chemical biology and chemogenomics and critically evaluates their current and future potential to impact drug discovery. Conclusions: Computational methods of high relevance for chemogenomics and chemical biology either derive knowledge from large-scale analysis of available drug and target data or interface experimental programs with predictive methods. Approaches for drug target prediction and the systematic analysis of polypharmacology substantially impact research in this area.     

Virtual-screening targeting Human Immunodeficiency Virus type 1 integrase-lens epithelium-derived growth factor/p75 interaction for drug development

Three integrase (IN) inhibitors have been approved by FDA for clinical treatment of Human Immunodeficiency Virus (HIV) infection. This stimulates more researchers to focus their studies on this target for anti-HIV drug development. Three steps regarding of IN activity have been validated for inhibitor discovery: strand transfer, 3′-terminal processing, and IN-lens epithelium-derived growth factor (LEDGF)/p75 interaction. Among them, IN-LEDGF/p75 interaction is a new target validated in recent years. Emergence of drug-resistant virus strains makes this target appealing to pharmacologists. Compared with the traditional screening methods such as AlphaScreen and cell-based screening developed for IN inhibitor discovery, virtual screening is a powerful technique in modern drug discovery. Here we summarized the recent advances of virtual-screening targeting IN-LEDFG/p75 interaction. The combined application of virtual screening and experiments in drug discovery against IN-LEDFG/p75 interaction sheds light on anti-HIV research and drug discovery.     

In silico research in drug discovery

Target and lead discovery constitute the main components of today's early pharmaceutical research. The aim of target discovery is the identification and validation of suitable drug targets for therapeutic intervention, whereas lead discovery identifies novel chemical molecules that act on those targets. With the near completion of the human genome sequencing, bioinformatics has established itself as an essential tool in target discovery and the in silico analysis of gene expression and gene function are now an integral part of it, facilitating the selection of the most relevant targets for a disease under study. In lead discovery, advances in chemoinformatics have led to the design of compound libraries in silico that can be screened virtually. Moreover, computational methods are being developed to predict the drug-likeness of compounds. Thus, drug discovery is already on the road towards electronic R&D.     

Peptide ligands in antibacterial drug discovery: use as inhibitors in target validation and target-based screening

There is an urgent need to develop novel classes of antibiotics to counter the inexorable rise of resistant bacterial pathogens. Modern antibacterial drug discovery is focused on the identification and validation of novel protein targets that may have a suitable therapeutic index. In combination with assays for function, the advent of microbial genomics has been invaluable in identifying novel antibacterial drug targets. The major challenge in this field is the implementation of methods that validate protein targets leading to the discovery of new chemical entities. Ligand-directed drug discovery has the distinct advantage of having a concurrent analysis of both the importance of a target in the disease process and its amenability to functional modulation by small molecules. VITA is a process that enables a target-based paradigm by using peptide ligands for direct in vitro and in vivo validation of antibacterial targets and the implementation of high-throughput assays to identify novel inhibitory molecules. This process can establish sufficient levels of confidence indicating that the target is relevant to the disease process and inhibition of the target will lead to effective disease treatment.     

药物发现过程中人工智能的应用研究进展

人工智能（AI）和机器学习不仅使药物发现和开发实现了质的飞跃,而且帮助药物开发进程进入现代化。机器学习和深度学习算法已应用于药物发现各个阶段,如先导化合物的筛选、多肽合成及小分子药物的发现、最佳给药剂量的确定、类药化合物的设计和药物不良反应的预测、蛋白质间相互作用的预测、虚拟筛选效率的提高、定量构效关系（QSAR）建模和药物重新定位、理化性质和药物靶标亲和力的预测、化合物的结合预测和体内安全性分析、多靶点配体药物分子的设计以及临床试验的设计。简要综述了AI算法和传统化学相结合以提高药物发现的效率以及AI在药物发现过程中的应用研究进展,以期为AI应用于药物发现提供一定参考。     

网络药理学:认识药物及发现药物的新理念

网络药理学是指将药物作用网络与生物网络整合在一起,分析药物在此网络中与特定节点或模块的相互作用关系,从而理解药物和机体相互作用的科学。网络药理学突破传统的"一个药物一个靶标,一种疾病"理念,代表了现代生物医药研究的哲学理念与研究模式的转变。以系统生物学和网络生物学基本理论为基础的网络药理学具有整体性、系统性的特点,注重网络平衡(或鲁棒性)和网络扰动,强调理解某个单一生物分子(如基因、mRNA或蛋白等)在生物体系中的生物学地位和动力学过程要比理解其具体生物功能更为重要,揭示药物作用的生物学和动力学谱要比揭示其作用的单个靶标或几个"碎片化"靶标更重要,对认识药物和发现药物的理念产生了深远影响。