药物靶标蛋白筛选的化学蛋白质组学研究进展

药物或生物活性物质通过与靶蛋白结合而发挥功能,研究表明,大多数药物具有多个作用靶点,药物靶标的发现有助于药物前体的筛选和作用机制的研究,同时对其耐药性等副作用的解决方案提供理论指导.基于生物质谱技术的蛋白质组学可对蛋白质进行高通量的定性定量分析,为药物靶标的筛选提供了全新的平台.本文综述了基于固载药物和游离药物模式的药物靶标蛋白筛选相关方法和应用研究的最新进展,为基于生物质谱技术的化学蛋白质组学研究提供参考.     

细胞质膜蛋白质组学研究技术进展

质膜蛋白在细胞中执行着非常重要的功能。随着蛋白质组学的发展,细胞质膜蛋白质组学成为蛋白质组学研究的重要组成部分,它为质膜蛋白的生物功能研究及药物靶标的发现提供了新的途径。然而,质膜蛋白丰度低、疏水性强,对现有蛋白质组学研究技术提出了挑战。简要综述了近年来质膜蛋白质组研究的相关技术进展,包括富集、提取分离鉴定方法及定量和生物信息学研究方法等。     

化学蛋白质组学技术在药物靶标鉴定中的应用

药物开发过程面临多重挑战,而靶标确证是其中的重要一环。如何运用多种研究方法发现和确认小分子药物的靶标是目前研究人员的主要工作内容之一。化学蛋白质组学整合了细胞生物学、合成化学和生物质谱等多门学科,为药物的靶标筛选提供了新平台。本文对近年来发展的基于生物质谱的化学蛋白质组学药物靶标鉴定技术进行了总结,结合具体应用分析其优缺点,并对该类技术的发展和应用进行总结和展望。     

《细胞-代谢》：研究发现戊二酸尿症潜在药物靶标

《细胞-代谢》（Cell Metabolism）报道。中科院上海药物所化学蛋白质组学研究中心科研人员首次发现了与遗传代谢性疾病戊二酸尿症相关的体内蛋白修饰新型通路——赖氨酸戊二酰化,并发现了治疗此疾病的潜在新药物靶标。     

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。     

药物靶标蛋白在蛋白网络中的分布

药物靶标发现是目前生物学研究领域的热点和难点问题。从已有药物靶标中寻找规律可以为新靶标的发现总结规律,提供依据。随着功能基因组学的发展,这种组学数据的积累为这一问题的研究提供了契机。本文研究了已有靶标在蛋白网络中的分布,并分析了它们的蛋白功能域组成情况。结果显示靶标基因倾向位于网络的核心区域,并且集中在一些特定蛋白家族中。这些规律的总结将对药物研发过程中药物靶点的选择提供一定的帮助。     

癌症差异蛋白质组学研究中样品分离和鉴定分析技术

随着人类基因组测序的完成,癌症研究的重点从基因组学转移到蛋白质组学研究中。癌症研究中的差异蛋白质组学技术也飞速发展,包括癌症样品制备、分离,蛋白质鉴定分析、蛋白质组定量研究和翻译后修饰研究等。这些技术极大地推动了与癌症相关的差异蛋白质组学研究,使蛋白质组学在癌症早期诊断、治疗,监测以及发现新药物治疗靶标方面发挥更大的作用。本文主要综述了近年来癌症差异蛋白质组学研究中样品分离和鉴定分析技术。     

药物蛋白质组学与药物发现

21世纪,科学家面临着从基因组到蛋白质组的转变,蛋白质组学是基因组和药物发现的效率。药物蛋白质组学研究不仅有助于发现治疗的可能靶点,也将明显提高药物发现的效率。药物蛋白质组学的研究内容,在临床前包括发现新的治疗靶点和发现针对所有靶点的全部化合物,在临床研究方面应包括药物作用的特异蛋白作为诊断和治疗的标志,或以蛋白质谱的差异来分类者。本文主要综述了蛋白质组学在药物靶点的发现和确认,以有药物发现过程中最有关的技术物研究进展。     

持续发展的生物技术药物

基因组学、蛋白质组学、生物信息学的研究．对疾病发生的信号转导与发病机理的阐明,和新的药物靶标的发现,极大地促进了生物技术药物的研究和发展,使其成为创新药物研发的主体。     

蛋白质组学及其在肿瘤研究中的应用

简要介绍了蛋白质组学的概念、研究方法及其在肿瘤研究中的应用.蛋白质组学研究直接定位于蛋白质水平,从整体、动态、定量的角度去研究基因的功能,是后基因组计划的一个重要组成部分.恶性肿瘤是一种多基因参与的复杂疾病,从蛋白质整体水平上研究恶性肿瘤将有助于进一步揭示恶性肿瘤的发病机制,发现恶肿瘤特异性的标志物及其药物治疗的靶标.     

Label-free optical biosensor: A tool for G protein-coupled receptors pharmacology profiling and inverse agonists identification

Current GPCR cell-based assays often rely on the measurement of a loaded fluorescent dye, fluorescently tagged targets, or the expression of a reporter. These manipulations may alter the cellular physiology of the target GPCR, and the measurements may be subject to off-target interference of compounds. Label-free optical biosensor-based technologies that provide a noninvasive methodology to study GPCR activation and signaling have been developed. These technologies enable the evaluation of drug effects on various GPCRs that couple to different signal transduction pathways using only one assay platform. This technology is highly sensitive and detects inverse agonism, therefore providing a convenient tool to study the pharmacology of drugs. Furthermore, its real-time kinetic measurements give researchers additional information about the biological responses induced by the drug. This assay platform when applied in early drug discovery efforts can provide valuable information on the mechanism of action and pharmacology profiles of drug candidates.     

Molecular drug targets and structure based drug design: A holistic approach

Access to the complete human genome sequence as well as to the complete sequences of pathogenic organisms provides information that can result in an avalanche of therapeutic targets. Structure-based design is one of the first techniques to be used in drug design. Structure based design refers specifically to finding and complementing the 3D structure (binding and/or active site) of a target molecule such as a receptor protein. The aim of this review is to give an outline of studies in the field of structure based drug design that has helped in the discovery process of new drugs. The emphasis will be on comparative/homology modeling.     

Dynamical modeling of drug effect using hybrid systems

Drug discovery today is a complex, expensive, and time-consuming process with high attrition rate. A more systematic approach is needed to combine innovative approaches in order to lead to more effective and efficient drug development. This article provides systematic mathematical analysis and dynamical modeling of drug effect under gene regulatory network contexts. A hybrid systems model, which merges together discrete and continuous dynamics into a single dynamical model, is proposed to study dynamics of the underlying regulatory network under drug perturbations. The major goal is to understand how the system changes when perturbed by drugs and give suggestions for better therapeutic interventions. A realistic periodic drug intake scenario is considered, drug pharmacokinetics and pharmacodynamics information being taken into account in the proposed hybrid systems model. Simulations are performed using MATLAB/SIMULINK to corroborate the analytical results.     

Target assessment for antiparasitic drug discovery

Drug discovery is a high-risk, expensive and lengthy process taking at least 12 years and costing upwards of US$500 million per drug to reach the clinic. For neglected diseases, the drug discovery process is driven by medical need and guided by pre-defined target product profiles. Assessment and prioritisation of the most promising targets for entry into screening programmes is crucial for maximising the chances of success. Here, we describe criteria used in our drug discovery unit for target assessment and introduce the 'traffic-light' system as a prioritisation and management tool. We hope this brief review will stimulate basic scientists to acquire additional information necessary for drug discovery.     

Drug discovery for malaria: a very challenging and timely endeavor

The prevalence of resistance to known antimalarial drugs has resulted in the expansion of antimalarial drug discovery efforts. Academic and nonprofit institutions are partnering with the pharmaceutical industry to develop new antimalarial drugs. Several new antimalarial agents are undergoing clinical trials, mainly those resurrected from previous antimalarial drug discovery programs. Novel antimalarials are being advanced through the drug development process, of course, with the anticipated high failure rate typical of drug discovery. Many of these are summarized in this review. Mechanisms for funding antimalarial drug discovery and genomic information to aid drug target selection have never been better. It remains to be seen whether ongoing efforts will be sufficient for reducing malaria burden in the developing world.     

Manipulation of the mouse genome: a multiple impact resource for drug discovery and development

Few would deny that the pharmaceutical industry's investment in genomics throughout the 1990s has yet to deliver in terms of drugs on the market. The reasons are complex and beyond the scope of this review. The unique ability to manipulate the mouse genome, however, has already had a positive impact on all stages of the drug discovery process and, increasingly, on the drug development process too. We give an overview of some recent applications of so-called 'transgenic' mouse technology in pharmaceutical research and development. We show how genetic manipulation in the mouse can be employed at multiple points in the drug discovery and development process, providing new solutions to old problems.     

Applications of high-throughput ADME in drug discovery

Assessment of physicochemical and pharmacological properties is now conducted at very early stages of drug discovery for the purpose of accelerating the conversion of hits and leads into qualified development candidates. In particular, in vitro absorption, distribution, metabolism and elimination (ADME) assays and in vivo drug metabolism pharmacokinetic (DMPK) studies are being conducted throughout the discovery process, from hit generation through to lead optimization, with the goal of reducing the attrition rate of these potential drug candidates as they progress through development. Because the continuing trend in drug discovery has been to access ADME information earlier and earlier in the discovery process, the need has arisen within the analytical community to introduce faster and better analytical methods to enhance the 'developability' of drug leads. Strategies for streamlined ADME assessment of drug candidates in discovery and pre-clinical development are presented within.     

Literature mining in support of drug discovery

The drug discovery enterprise provides strong drivers for data integration. While attention in this arena has tended to focus on integration of primary data from omics and other large platform technologies contributing to drug discovery and development, the scientific literature remains a major source of information valuable to pharmaceutical enterprises, and therefore tools for mining such data and integrating it with other sources are of vital interest and economic impact. This review provides a brief overview of approaches to literature mining as they relate to drug discovery, and offers an illustrative case study of a 'lightweight' approach we have implemented within an industrial context.     

天然活性成分作用靶点识别及确证方法的研究进展

药物靶标的发现和验证是新药研发的关键环节,对新药创制具有源头创新意义。天然产物是新药创制的重要来源,识别其作用靶点不仅为临床预防治疗提供可能新策略,也为进一步阐释中草药及其复方的作用特点及分子机制提供参考依据。随着生命科学和信息学的发展,药物靶点的识别及确证方法不断涌现,生物信息学、网络药理学、蛋白质组学、亲和色谱、药物亲和稳定性、芯片技术、基因敲除技术、RNA干扰等技术的广泛应用,越来越多的天然活性成分的靶点得以识别和验证。因此,本文对近五年来天然活性成分作用靶点识别及确证方法做一简要综述,以供参考。     

Integrated technology platform protein kinases for drug development in oncology

Protein kinases are among the most promising targets for drug discovery and development, mostly in oncology but also in other fields such as inflammation, Alzheimer's, and infectious diseases. The Integrated Technology Platform Protein Kinases was designed as a comprehensive tool for drug discovery in thefield of oncology. It combines modules for the identification and validation of novel target protein kinases, a unique panel of active recombinant protein kinases, high-throughput screening, selectivity profiling, cellular testing, and in vivo tumor models. Here we give an overview of the Integrated Technology Platform Protein Kinases as well as data that validate each module.