基于受体的药物高通量筛选研究进展

受体是药物筛选的重要靶标,基于受体的药物高通量筛选是药物筛选的主要类型之一。本文根据受体作用原理,按照检测对象的不同,从直接与间接检测的角度,将基于受体的药物高通量筛选进行了分类,总结了基于受体的几种不同的药物筛选模型,并简要介绍了高通量筛选技术在中药研究中的应用,对药物筛选的发展进行了展望。     

以Neprilysin蛋白酶为靶标的药物筛选模型的建立及应用

目的:建立以蛋白酶Neprilysin(NEP)为靶点的高通量药物筛选模型,应用该模型筛选抑制剂。方法:利用毕赤酵母表达系统。构建重组质粒p PICZα-A-NEP,表达载体通过与酵母菌X-33基因组染色体发生同源重组,将外源基因整合于染色体后实现目的蛋白的表达。应用荧光共振能量转移法(FRET)检测蛋白酶活性,优化反应条件,建立药物筛选体系,筛选抑制剂。结果:成功构建表达载体p PICZα-A-NEP;建立了以NEP为靶标的药物筛选模型,获得模型反应动力学参数Vmax=3.6μM/s,Kcat/Km=4.5×105M-1s-1,测定模型Z-因子为0.89,说明体系稳定可用于以NEP为靶标的药物的高通量筛选;并用该模型对天然产物组分库进行筛选,在0.5mg/ml的药物浓度下,得到抑制率较高的药物为4种,并测得半数抑制浓度IC50值,其中MDCNCL01000242的IC50值最低,为(8.31±0.03)μg/ml。结论:建立的药物筛选模型较为理想,适用于NEP抑制剂的筛选,可促进药物的研发。     

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。     

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.     

基于流感病毒PA_N蛋白的高通量药物筛选

流感病毒的PA N蛋白高度保守,并且具有核酸内切酶活性,是抗流感药物研发的潜在靶点。通过高通量药物筛选体系,从372种化合物中筛选出3种对流感病毒H5N1的PA N蛋白抑制作用较好的化合物。将这3种化合物分别与PA N蛋白进行分子对接模拟,结果显示它们均可以与PA N蛋白活性位点的二价金属离子和氨基酸残基相互作用,从而为抗流感病毒药物的发现提供了先导化合物。     

以STAT3为靶标的抗肿瘤药物高通量筛选模型的建立和应用

旨在建立稳定可靠的以转导与转录激活子(STAT3)为靶标的抗肿瘤高通量筛选模型,应用该模型筛选潜在的抗癌药物。利用基因重组、蛋白表达纯化技术,获得STAT3目的蛋白,使用酶联免疫吸附法(ELISA)进行高通量药物筛选,将筛选出的抑制剂在细胞水平上利用MTT比色法测定化合物对癌细胞增殖的影响。结果显示,成功构建表达载体pET-28a-STAT3;所建立的模型稳定可行,可用于以STAT3为靶标的抗肿瘤药物的高通量筛选;用该模型对8 248个样品进行筛选,在500μmol/L药物浓度下,化合物MDC6抑制率为92%,另外,进行IC50值的测定时,分子水平上最低达到3.37μmol/L,在细胞水平上可达到15.92μmol/L。建立的高通量药物筛选模型,具有操作方便、成本低、结果稳定等特点,可用于STAT3抑制剂的大规模筛选。     

高通量药物筛选技术的研究现状及光学编码技术在其中的应用

高通量药物筛选是创新药物研究的重要内容,本文综合介绍了高通量药物筛选的研究现状及发展趋势,及目前遇到的一些问题,如随着样品的体积达到微升量级时,目前所采用的孔板筛选遇到了一些难以克服的障碍,使化合物和药物靶点的相互作用难以进行等。针对这些问题,本文介绍了一种新的以自编码光谱识别微球为基础的新型高通量药物筛选技术在高通量药物筛选中的应用,同时简要介绍了我们目前所做的一些工作。     

膜片钳技术的新进展及其在药物高通量筛选中的应用

作为先进的细胞电生理技术,膜片钳一直被奉为研究离子通道的“金标准”。应用膜片钳技术可以证实细胞膜上离子通道的存在并能对其电生理特性、分子结构、药物作用机制等进行深入的研究。基因组学、蛋白质组学研究表明,以离子通道为靶标的药物研究在未来具有很大的发展空间。为了突破由于筛选技术所造成的针对离子通道为靶标的药物研发的瓶颈,近年来,对膜片钳技术进行了改进以适合药物高通量筛选的需求,由此产生了一些新的技术。本文就最近几年膜片钳技术的新进展及其在药物高通量筛选中的应用进行了综述。     

高通量药物筛选技术进展及新药开发策略

新药研发过程中．通过筛选而获得具有生物活性的先导化合物．是创新药物研究的关键．目前药物筛选模型已经从传统的整体动物、器官和组织水平发展到细胞和分子水平。创新药物的发现都离不开采用适当的药物作用靶点对大量化合物样品进行筛选．而且筛选规模越大,发现新药的机会就越多。随着计算机技术、生物芯片、蛋白质组学、组合化学等的发展．高通量药物筛选技术应运而生。高通量筛选体系在创新药物筛选中的应用是新药开发研究的一个重要领域。     

蛋白酪氨酸磷酸酶1B抑制剂高通量筛选模型建立和应用

本文以蛋白酪氨酸磷酸酶1B（protein tyrosine phosphatase,PTP1B）作为靶点,采用分子克隆GST融合蛋白的方法,重组表达获得PTP1B,结合自动化操作技术和比色分析,建立了一种PTP1B抑制剂的高通量筛选模型.经在96孔板优化各种反应条件并对17940个样品的筛选结果表明,靶向PTP1B建立的高通量筛选模型具有微量、快速、特异、灵敏等特点,平均日筛选量可达15000样次以上,为寻找新的抗糖尿病药物和先导化合物提供了一种先进的手段.     

代谢组学在肿瘤药物靶点筛选中的应用进展

肿瘤是一种多因素参与造成机体各系统功能平衡紊乱的代谢性疾病,代谢重编程是恶性肿瘤的重要特征之一.研究"代谢指纹图谱"的代谢组学,通过揭示肿瘤或药物引起的宿主内源性代谢物的变化,为肿瘤药物靶点的筛选提供了可能.但目前对代谢组在肿瘤药物靶点筛选中的整体性综述并不多见,因此,本文在介绍了代谢组学筛选肿瘤药物靶点的流程的基础上...     

秀丽隐杆线虫（Caenorhabditis elegans）在药物筛选中的应用

基于靶点的体外药物筛选操作相对简单,成本较低,但是由于药物在体内的作用并不仅仅取决于其与靶点的作用程度,吸收、分布、代谢、排泄特征和毒性均会对早期先导物能否进入临床使用产生极大的影响,因此,药物的体内筛选受到重视。本文重点综述了秀丽隐杆线虫（C.elegans）在抗衰老、抗感染药物筛选中的应用情况。秀丽隐杆线虫结构简单、易于培养和可实现高通量筛选,在未来的药物筛选中必将发挥更重要的作用。     

Strategic genome-scale prioritization of unique drug targets: A case study of Streptococcus gordonii

The current reach of genomics extends facilitated identification of microbial virulence factors, a primary objective for antimicrobial drug and vaccine design. Many putative proteins are yet to be identified which can act as potent drug targets. There is lack and limitation of methods which appropriately combine several omics ways for putative and new drug target identification. The study emphasizes a combined bioinformatic and theoretical method of screening unique and putative drug targets, lacking similarity with experimentally reported essential genes and drug targets. Synteny based comparison was carried out with 11 streptococci considering S. gordonii as reference genome. It revealed 534 non-homologous genes of which 334 were putative. Similarity search against host proteome, metabolic pathway annotation and subcellular localization predication identified 16 potent drug targets. This is a first attempt of several combinational approaches of similarity search with target protein structural features for screening drug targets, yielding a pipeline which can be substantiated to other human pathogens.     

Efficient Modeling and Active Learning Discovery of Biological Responses

High throughput and high content screening involve determination of the effect of many compounds on a given target. As currently practiced, screening for each new target typically makes little use of information from screens of prior targets. Further, choices of compounds to advance to drug development are made without significant screening against off-target effects. The overall drug development process could be made more effective, as well as less expensive and time consuming, if potential effects of all compounds on all possible targets could be considered, yet the cost of such full experimentation would be prohibitive. In this paper, we describe a potential solution: probabilistic models that can be used to predict results for unmeasured combinations, and active learning algorithms for efficiently selecting which experiments to perform in order to build those models and determining when to stop. Using simulated and experimental data, we show that our approaches can produce powerful predictive models without exhaustive experimentation and can learn them much faster than by selecting experiments at random.     

Screening essential genes of Mycobacterium tuberculosis with the pathway enrichment method

The number of effective drugs for the prevention and control of tuberculosis is very limited. Therefore, high-throughput screening for Mycobacterium tuberculosis drug targets is critical. In addition, determining the essential gene cluster is important for both understanding a survival mechanism and finding novel molecular targets for anti-tuberculosis drugs. In this study, we applied the pathway enrichment method to perform high throughput screening of genes encoding key molecules for potential drug targets for M. tuberculosis. Our results indicated 122 genes that existed in more than three pathways, while four existed in 11 pathways. We predicted 55 genes that are potentially essential genes. Four of them, namely, Rv0363c, Rv0408, Rv0409 and Rv0794c, had the highest probability to be essential genes, and thus further experimental validation is warranted.     

In silico prediction of drug targets in <Emphasis Type="Italic">Vibrio cholerae</Emphasis>

Identification of potential drug targets is the first step in the process of modern drug discovery, subjected to their validation and drug development. Whole genome sequences of a number of organisms allow prediction of potential drug targets using sequence comparison approaches. Here, we present a subtractive approach exploiting the knowledge of global gene expression along with sequence comparisons to predict the potential drug targets more efficiently. Based on the knowledge of 155 known virulence and their coexpressed genes mined from microarray database in the public domain, 357 coexpressed probable virulence genes for Vibrio cholerae were predicted. Based on screening of Database of Essential Genes using blastn, a total of 102 genes out of these 357 were enlisted as vitally essential genes, and hence good putative drug targets. As the effective drug target is a protein which is only present in the pathogen, similarity search of these 102 essential genes against human genome sequence led to subtraction of 66 genes, thus leaving behind a subset of 36 genes whose products have been called as potential drug targets. The gene ontology analysis using Blast2GO of these 36 genes revealed their roles in important metabolic pathways of V. cholerae or on the surface of the pathogen. Thus, we propose that the products of these genes be evaluated as target sites of drugs against V. cholerae in future investigations.     

Fragment-based activity space: smaller is better

Fragment-based drug discovery has the potential to supersede traditional high throughput screening based drug discovery for molecular targets amenable to structure determination. This is because the chemical diversity coverage is better accomplished by a fragment collection of reasonable size than by larger HTS collections. Furthermore, fragments have the potential to be efficient target binders with higher probability than more elaborated drug-like compounds. The selection of the fragment screening technique is driven by sensitivity and throughput considerations, and we advocate in the present article the use of high concentration bioassays in conjunction with NMR-based hit confirmation. Subsequent ligand X-ray structure determination of the fragment ligand in complex with the target protein by co-crystallisation or crystal soaking can focus on confirmed binders.     

G蛋白偶联受体计算研究的进展和前瞻

G蛋白偶联受体(G protein-coupled receptor,GPCR)是含有七个跨膜螺旋的一类重要蛋白,是迄今为止发现的最大的多药物靶标受体超蛋白家族。例如,目前上市药物中有超过30%是以GPCR为靶点的。然而,与GPCR重要性形成强烈反差的是科学界对于其结构与功能的了解非常贫乏,主要原因是通过实验手段来获得GPCR的结构与功能信息极其困难。利用生物信息学方法从基因组规模的数据中识别GPCR并预测三维结构是可行途径之一。基于生物信息学的GPCR研究将为新型药物靶标的筛选和药物的开发提供一定的帮助。本文论述了几种较为典型的GPCR计算方法,并基于已有研究提出可能的创新性研究策略来解决GPCR蛋白识别、跨膜区定位、以及结构和功能预测等问题。     

Progress in functional genomics approaches to antifungal drug target discovery

Antifungal drug discovery is starting to benefit from the enormous advances in the genomics field, which have occurred in the past decade. As traditional drug screening on existing targets is not delivering the long-awaited potent antifungals, efforts to use novel genetics and genomics-based strategies to aid in the discovery of novel drug targets are gaining increased importance. The current paradigm in antifungal drug target discovery focuses on basically two main classes of targets to evaluate: genes essential for viability and virulence or pathogenicity factors. Here we report on recent advances in genetics and genomics-based technologies that will allow us not only to identify and validate novel fungal drug targets, but hopefully in the longer run also to discover potent novel therapeutic agents. Fungal pathogens have typically presented significant obstacles when subjected to genetics, but the creativity of scientists in the anti-infectives field and the cross-talk with scientists in other areas is now yielding exciting new tools and technologies to tackle the problem of finding potent, specific and non-toxic antifungal therapeutics.     

Biochemical composition and metabolic pathways of filarial worms Setaria cervi: search for new antifilarial agents

The main problem regarding the chemotherapy of filariasis is that no safe and effective drug is available yet to combat the adult human filarial worms. Setaria cervi, the causal organism of setariasis and lumbar paralysis in cattle, is routinely employed as a model organism for conducting biochemical and enzymatic studies on filarial parasites. In view of the practical difficulties in procuring human strains of Wuchereria bancrofti and Brugia malayi for drug screening, the bovine filarial parasite S. cervi, resembling the human species in having microfilarial periodicity and chemotherapeutic response to known antifilarial agents, is widely used as a model in such studies. For a rational approach to antifilarial chemotherapy, knowledge of the biochemical composition and metabolic pathways of this helminth parasite may be of paramount importance, so that more potent antifilarial agents based on specific drug targets can be identified in drug discovery programmes. The present review provides an update on the biochemistry of the important metabolic pathways functioning within this potentially important bovine parasite, that have so far been studied, and on those that need to be investigated further so as to identify novel drug targets that can be exploited for designing new antifilarial drugs.