蛋白质酪氨酸磷酸酶与药物靶标

蛋白质酪氨酸磷酸化作用是真核细胞中的一种重要信号作用机制,由蛋白质酪氨酸激酶和蛋白质酪氨酸磷酸酶共同调控.蛋白质酪氨酸磷酸酶在真核细胞代谢进程中起着重要的作用,与许多人类疾病如肿瘤、心血管疾病、免疫缺陷性疾病、传染病、神经性以及代谢方面疾病的发病机制密切相关,许多蛋白质酪氨酸磷酸酶已成为研究和开发治疗人类重大疾病药物的优秀靶标.     

基于生物信息学方法发现潜在药物靶标

药物靶点通常是在代谢或信号通路中与特定疾病或病理状态有关的关键分子.通过绑定到特定活动区域抑制这个关键分子进行药物设计.确定特定疾病有关的靶标分子是现代新药开发的基础.在药物靶标发现的过程中,生物信息学方法发挥了不可替代的重要的作用,尤其适用于大规模多组学数据的分析.目前,已涌现了许多与疾病相关的数据库资源,基于生物网络特征、多基因芯片、蛋白质组、代谢组数据等建立了多种生物信息学方法发现潜在的药物靶标,并预测靶标可药性和药物副作用.     

利用靶标分子使药物释放优化

ＢｉｏｔｅｃｈｎｏｌｏｇｙＮｅｗｓ ,2 0 0 1年 2 1卷 15期报道 :专化性在开发有效药物中是一个关键。刚成立 6个月的ＴａｒｇｅｔｅｄＭｏｌｅｃｕｌｅｓＣｒｏｐ .(ＴＭＣ) (Ｓａｎｄｉｅｇｏ ,ＣＡ)在药物专化性释放研究领域中是一个新加入者。该公司针对血管专化性靶标来改善药物在体内的有效释放 ,因而该公司成为研究肿瘤学、血管系统疾病和炎症的公司的新伙伴。ＴＭＣ是去年 11月从ＢｕｒｎｈａｍＩｎｓｔｉｔｕｔｅ(ＬａＪｏｌｌａ ,ＣＡ)独立出来的研究开发型公司 ,拥有Ｂｕｒｎｈａｍ公司的 12个专利和一个有专利保护的技术所生…     

化合物药物-靶标蛋白互作关联预测算法进展分析

药物的使用极大地提高了人类的生存质量。药物的有效性是药物发现研究中的关键环节。药物的有效性通过识别药物与其作用的靶标蛋白来判断。然而,通过高通量筛选的实验方法分析确定化合物药物-靶标蛋白互作关联是一个十分昂贵、耗时且富有挑战性的任务。基于计算方法的化合物药物-靶标蛋白互作关联预测研究具有效率高、成本低的特点,越来越受到人们的重视。相比实验验证方法,化合物药物-靶标蛋白互作关联的计算方法可为药物发现研究后续的生物药学实验提供更为准确的潜在化合物药物-靶标蛋白候选对,达到减少生物实验的时间和成本的目的。本文回顾了近20年来基于计算方法的化合物药物-靶标蛋白互作关联预测算法所涉及的生物医学特征数据、预测方法和技术,并分析研究过程中所面临的生物医学特征数据高维稀疏,以及多源生物医学数据融合程度不高等问题,为进一步研究提供有价值的参考。     

小分子药物靶标寻找方法研究进展

许多微生物的次生代谢物属于小分子活性化合物,在医疗及农业领域发挥着重要的作用。在基因组学、蛋白质组学与生物信息学等技术的推动下,一些新的小分子药物靶标寻找方法应运而生了,这些新的方法主要是基于细胞中基因或蛋白质的表达量、蛋白质的亲和性、稳定性等各种特性进行靶标寻找的。小分子药物靶标寻找方法的发展加快了阐明小分子药物作用机理的历程,也为发现新的靶标资源以便于进一步筛选活性更高的药物提供了技术保障。     

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。     

分枝杆菌膜蛋白相关抗结核药物靶标的研究进展

结核病是由结核分枝杆菌引起的慢性传染病。当前结核病耐药等问题愈加严重,基于新靶点的抗结核新药研发也变得尤为重要。分枝杆菌膜蛋白是镶嵌于细胞膜磷脂双层或与脂双层结合的一类蛋白,参与细胞结构合成、物质跨膜转运、催化等重要的生物学功能,并在宿主感染中参与免疫识别、免疫逃逸、毒力因子释放等致病机制。另一方面,膜蛋白具有特定的拓扑结构和细胞定位,药物靶标可及性强,因此膜蛋白是抗结核药物作用的理想靶标。本文就分枝杆菌膜蛋白在细胞壁合成、物质跨膜转运、细菌休眠、细菌与宿主互作及分泌系统相关研究进展进行综述,旨在为抗结核药物研发提供新思路。     

探索药物靶标，方便医药开发

在医药界,药物上市可以说是一场斗智斗力的较量。虽然许多医药公司采取收购其他公司产权的策略,但是他们的研发部门仍须采取诸多激励措施来促进创新。不管是发现一类新的化合物,还是找到一个新的药物靶标,抑或是调整方案优化某个化合物,创新都是不可或缺的。     

光亲和标记技术在药物发现中的应用

功能蛋白质组学的研究在药物发现中扮演着重要的角色,而光亲和标记技术是研究功能蛋白质组学的主要策略之一,它主要有两个方面的应用：靶标蛋白的确定和活性小分子配体与靶标蛋白作用模式的揭示,这些信息为药物的发现提供了强有力的支持。     

基于细胞热漂移测定(CETSA)技术鉴定活细胞内药物靶标的标准操作流程

【目的】细胞热漂移测定(cell thermal shift assay, CETSA)技术是一种检测细胞内药物(配体)和蛋白质(靶标)相互作用的技术,原理是当蛋白质结合药物后,其热稳定性会发生变化,通过测定这种变化去鉴定药物和蛋白之间的相互作用。本研究以治疗多发性骨髓瘤的靶向药帕比司他(panobinostat)为例,建立基于蛋白印迹杂交(Western blotting)和CETSA技术的药物靶蛋白鉴定的标准操作流程。【方法】首先用药物panobinostat处理培养的K562细胞,然后加热处理细胞、裂解细胞及提取可溶性蛋白,以及用抗靶蛋白的抗体经Western blotting定量可溶性蛋白。【结果】经Western blotting定量及曲线拟合,成功得到3个蛋白——组蛋白去乙酰化酶(histone deacetylase,HDAC1)、人突触蛋白(humansyntaxin-4,STX4)以及四三肽重复结构域(tetratricopeptiderepeat domain38,TTC38)随温度变化的热熔解曲线和恒定温度条件下的药物剂量反应曲线。【结论】HDAC1、STX4及T...     

Knitting and untying the protein network: Modulation of protein ensembles as a therapeutic strategy

Proteins constitute the working machinery and structural support of all organisms. In performing a given function, they must adopt highly specific structures that can change with their level of activity, often through the direct or indirect action of other proteins. Indeed, proteins typically function within an ensemble, rather than individually. Hence, they must be sufficiently flexible to interact with each other and execute diverse tasks. The discovery that errors within these groups can ultimately cause disease has led to a paradigm shift in drug discovery, from an emphasis on single protein targets to a holistic approach whereby entire ensembles are targeted.     

Structure-based design of anti-mycobacterial drug leads that target the mycolic acid transporter MmpL3

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The architectural design of networks of protein domain architectures

Protein domain architectures (PDAs), in which single domains are linked to form multiple-domain proteins, are a major molecular form used by evolution for the diversification of protein functions. However, the design principles of PDAs remain largely uninvestigated. In this study, we constructed networks to connect domain architectures that had grown out from the same single domain for every single domain in the Pfam-A database and found that there are three main distinctive types of these networks, which suggests that evolution can exploit PDAs in three different ways. Further analysis showed that these three different types of PDA networks are each adopted by different types of protein domains, although many networks exhibit the characteristics of more than one of the three types. Our results shed light on nature''s blueprint for protein architecture and provide a framework for understanding architectural design from a network perspective.     

In vivo drug target discovery: identifying the best targets from the genome

A vast number of genes of unknown function threaten to clog drug discovery pipelines. To develop therapeutic products from novel genomic targets, it will be necessary to correlate biology with gene sequence information. Industrialized mouse reverse genetics is being used to determine gene function in the context of mammalian physiology and to identify the best targets for drug development.     

Autoinhibited proteins as promising drug targets

Current drug discovery efforts generally focus on a limited number of protein classes, typically including proteins with well-defined catalytic active sites (e.g., kinases) or ligand binding sites (e.g., G protein-coupled receptors). Nevertheless, many clinically important pathways are mediated by proteins with no such obvious targets for small molecule inhibitors. Allosteric inhibitors offer an alternative approach to inhibition of protein activities, particularly for proteins that undergo conformational changes as part of their activity cycle. Proteins regulated by autoinhibitory domains represent one broad class of proteins that meets this criterion. In this article, we discuss the potential of autoinhibited proteins as targets for allosteric inhibitors and describe two examples of small molecules that act by stabilizing native autoinhibited conformations of their targets. We propose that proteins regulated by autoinhibition may be generally amenable to allosteric inhibition by small molecules that stabilize the native, autoinhibited fold.     

抗结核活性化合物HY-152E的作用靶点分析

抗结核活性化合物HY-152E是本实验室前期获得的具有良好抗结核活性并拥有授权专利（ZL201210088290.0）的小分子化合物（最低抑菌浓度≤0.09 μg/mL）。为深入探索HY-152E的抗结核机制,本研究利用药物亲和反应靶标稳定性（drug affinity responsive target stability,DARTS）技术并结合蛋白质谱技术,分析可能与HY-152E相互作用的结核分枝杆菌潜在靶标蛋白。将结核分枝杆菌H37Ra的菌体蛋白裂解液与HY-152E共同孵育互作,用不同浓度的链霉菌蛋白酶消化30、45、60 min后,十二烷基硫酸钠-聚丙烯酰胺凝胶电泳（sodium dodecyl sulfate-polyacrylamide gel electrophoresis,SDS-PAGE）分离并比较与HY-152E互作前后菌体蛋白耐受蛋白酶消化的差异条带,分别在相对分子质量70 000和45 000~55 000处观察到差异蛋白条带。利用蛋白质谱技术分析差异条带的蛋白信息,共获得86个蛋白信息。结合结核分枝杆菌数据库及蛋白功能信息,最终筛选到9个蛋白可能是HY-152E的抗结核作用潜在靶标。这些潜在靶点的确定,为后续研究HY-152E的抗结核分子机制奠定了基础。     

Novel tracking function of moving target using chaotic dynamics in a recurrent neural network model

Chaotic dynamics introduced in a recurrent neural network model is applied to controlling an object to track a moving target in two-dimensional space, which is set as an ill-posed problem. The motion increments of the object are determined by a group of motion functions calculated in real time with firing states of the neurons in the network. Several cyclic memory attractors that correspond to several simple motions of the object in two-dimensional space are embedded. Chaotic dynamics introduced in the network causes corresponding complex motions of the object in two-dimensional space. Adaptively real-time switching of control parameter results in constrained chaos (chaotic itinerancy) in the state space of the network and enables the object to track a moving target along a certain trajectory successfully. The performance of tracking is evaluated by calculating the success rate over 100 trials with respect to nine kinds of trajectories along which the target moves respectively. Computer experiments show that chaotic dynamics is useful to track a moving target. To understand the relations between these cases and chaotic dynamics, dynamical structure of chaotic dynamics is investigated from dynamical viewpoint.     

Divide and conquer: subproteomic approaches toward gastric cancer biomarker and drug target discovery

The discovery of biomarkers for early detection and treatment for gastric cancer are two important gaps that proteomics have the potential to fill. Advancements in mass spectrometry, sample preparation and separation strategies are crucial to proteomics-based discoveries and subsequent translations from bench to bedside. A great number of studies exploiting various subproteomic approaches have emerged for higher-resolution analysis (compared with shotgun proteomics) that permit interrogation of different post-translational and subcellular compartmentalized forms of the same proteins as determinants of disease phenotypes. This is a unique and key strength of proteomics over genomics. In this review, the salient features, competitive edges and pitfalls of various subproteomic approaches are discussed. We also highlight valuable insights from several subproteomic studies that have increased our understanding of the molecular etiology of gastric cancer and the findings that led to the discovery of potential biomarkers/drug targets that were otherwise not revealed by conventional shotgun expression proteomics.