酵母双杂交系统筛选CLN8P相互作用蛋白

目的：应用酵母双杂交系统筛选CLN8P相互作用蛋白质,通过对相互作用蛋白质的筛选及研究,探讨CLN8的功能,为NCL8和NCLs疾病群的发病机制研究提供线索,并为疾病的蛋白质相互作用网络提供资料。方法 应用酵母双杂交技术,以pLexA-CLN8为诱饵质粒筛选人胎脑cDNA文库,得到Leu+LacZ+阳性克隆,进一步进行验证,并对验证后的阳性克隆的外源性片断进行测序及同源性分析。结果 从人胎脑cDNA文库中筛选得到60个Leu+LacZ+阳性克隆;将获得的具有外源性片段的文库质粒与诱饵质粒一对一重新转入酵母体内对相互作用进行验证,共获得阳性克隆22个;对验证后阳性克隆测序并进行同源性分析,共获得不同的候选基因序列10个。结论 应用酵母双杂交系统,共筛选得到10个不同的基因,其编码蛋白与CLN8P有相互作用,可能与NCLs 发病机制相关。     

酵母三杂交技术的应用策略及在病毒研究中的应用

从酵母双杂交系统衍生出来的酵母三杂交系统是一种主要用于研究细胞内RNA-蛋白质相互作用强大的技术方法,也为病毒相关研究,尤其为研究病毒生活史中病毒RNA与病毒蛋白和宿主细胞蛋白的相互作用提供了新的工具。本文重点介绍了此种形式的酵母三杂交系统的原理、缺陷、应用和应用策略。另外,本文还介绍了同时发展起来的另外两种形式的酵母三杂交系统,即分别用于研究小分子配体-受体相互作用、复杂蛋白质相互作用的酵母三杂交系统。     

酵母三杂交技术的应用策略及在病毒研究中的应用

从酵母双杂交系统衍生出来的酵母三杂交系统是一种主要用于研究细胞内RNA-蛋白质相互作用强大的技术方法,也为病毒相关研究,尤其为研究病毒生活史中病毒RNA与病毒蛋白和宿主细胞蛋白的相互作用提供了新的工具。本文重点介绍了此种形式的酵母三杂交系统的原理、缺陷、应用和应用策略。另外,本文还介绍了同时发展起来的另外两种形式的酵母三杂交系统,即分别用于研究小分子配体-受体相互作用、复杂蛋白质相互作用的酵母三杂交系统。     

酵母双杂交技术筛选并回转验证在胞内与PML-C结构域相互作用的蛋白

目的 利用酵母双杂交技术在活细胞内筛选并回转验证与PML-C结构域相互作用的蛋白质.方法 通过诱饵质粒pGBKT7-PML-C,利用酵母双杂交系统从白血病细胞cDNA文库中筛选与PML-C结构域相互作用的蛋白质.结果 利用酵母双杂交技术筛选到43个能与PML-C结构域相互作用的克隆;经进一步的归类与酵母回转试验得到9个阳性克隆.结论 在细胞内PML-C结构域能与多种蛋白质有相互作用.中性粒细胞弹性蛋白酶(neutrophil elastase,NE)介导的急性早幼粒细胞白血病的发生可能与这些相互作用所致的生物学功能改变有关.     

酵母三杂交系统:一种在真核细胞水平研究RNA-蛋白质相互作用的有效方法

RNA-蛋白质之间的相互作用是真核细胞RNA在细胞内执行其功能的基础,这种相互作用在基因表达的多个水平上均发挥着重要的调控作用。要研究RNA对基因表达的调控就必须对RNA-蛋白质之间相互作用的方式进行研究。本文介绍的酵母三杂交系统(yeast three-hybrid system)是一种近年来在酵母双杂交系统的基础上发展而来的,可用于鉴定先前未知的,在自然界中存在且具有生物学意义的RNA-蛋白质相互作用的实验方法。该方法提供了一个在真核细胞内研究RNA-蛋白质相互作用的技术平台。它可以根据酵母细胞所表现出的简单表型及易于辨别的酶反应,对体内复杂环境中发生的RNA-蛋白质的相互作用进行遗传学分析。     

利用噬菌体展示技术研究功能基因组及其与酵母双杂交方法的比较

 近年来基因组测序工作已揭示了数以万计的新基因,但是这些基因序列的价值只有当这些数据被翻译成蛋白质功能的时候才能被了解。而蛋白质与蛋白质之间相互作用构成了细胞生化反应网络的一个主要组成部分,不仅可以从分子水平揭示蛋白质的功能,而且为我们更好地理解生命过程﹑疾病机制和新药开发等提供了一个很好的基础。当前随着人类基因组和众多生物物种全基因组测序的完成,功能基因组学（functional genomics）成为研究的主流,它是对基因组中包含的全部基因及其所翻译的蛋白质的功能加以解读和描述,尤其是大量未知基因的功能及其对应的蛋白质产物的功能。在研究功能基因组方面已有许多生物化学和生物物理方法,其中噬菌体展示（phage display technology,PDT）和酵母双杂交（yeast two-hybrid system,Y2H）是比较成熟的生物技术。噬菌体展示技术诞生于 1985 年,在抗体库展示方面取得了巨大的成功,也被用于研究蛋白质-蛋白质相互作用和未知基因克隆等多个分子生物学领域^[1]。酵母双杂交方法诞生于 1989 年,它被广泛用于研究功能基因组和蛋白质-蛋白质相互作用,但是噬菌体展示技术在研究蛋白质-蛋白质相互作用方面具有一些独特的优势^[2]。本文综述了噬菌体展示技术在克隆功能基因和研究蛋白质-蛋白质相互作用方面的最新进展,同时将噬菌体展示技术与酵母双杂交方法进行了比较。     

酵母双杂交系统筛选GATA-1相互作用蛋白质及功能验证

目的 利用酵母双杂交技术从人脑cDNA文库中筛选与人GATA-1相互作用的蛋白质.方法 从人K562细胞中扩增出全长GATA1基因,设计引物将其3段截断体亚克隆入酵母表达载体pDBLeu中,转化至AH109感受态酵母中,利用酵母双杂交技术筛选人脑cDNA文库中与其相互作用的蛋白质,阳性克隆通过回转及免疫共沉淀试验进行验证,利用3xGATA荧光素酶报告基因对相互作用蛋白质进行功能验证.结果 成功构建出酵母诱饵蛋白表达质粒pDBLeu-GATA1(1),pDBLeu-GATA1(2),pDBLeu-GATA1(3),筛到34个阳性克隆,用生物信息学分析及回转验证得到5个与GATA-1相互作用的候选蛋白,通过免疫共沉淀试验进一步验证,获得3个蛋白质能与GATA-1相互作用,分别是ECSIT,EFEMP1和GPS2.荧光素酶试验表明这3个蛋白质均能对GATA1的转录活性产生影响,证实它们之间的相互作用具有影响GATA1转录的功能.结论 应用酵母双杂交技术及免疫共沉淀试验,从人脑cDNA文库中成功获得3个与GATA-1相互作用并对其转录活性具有调节作用的蛋白质,为研究GATA1蛋白质的功能提供了新的线索.     

基于K近邻的蛋白质功能的预测方法

蛋白质功能预测是后基因组时代研究的重要问题之一.利用蛋白质相互作用网络,提出了一种基于K近邻的蛋白质功能的注释方法,该方法首先计算待注释的蛋白质与所有已知功能的蛋白质间的注释环境相似度,选择其中最相似的K个蛋白质,将该K个蛋白质的功能注释进行加权平均,作为待注释的蛋白质最终的功能注释.在构建的芽殖酵母的两组大规模相互作用数据集上的测试表明,该方法能够有效的对蛋白质功能进行预测,在蛋白质功能预测性能上优于现有的一些方法.     

人类14-3-3ζ和GCH1蛋白相互作用的初步研究

目的 寻找人类14-3-3ζ蛋白的相互作用蛋白,为进一步揭示14-3-3ζ的作用机理提供线索.方法 以14-3-3ζ为"诱饵",利用酵母双杂交系统筛选人脑cDNA文库得到"猎物"蛋白,通过GST pulldown技术和哺乳动物细胞内免疫共沉淀技术验证14-3-3ζ和"猎物"蛋白的特异性结合.结果 首次利用酵母双杂交cDNA文库筛选技术发现了14-3-3ζ能够与GTP环化水解酶Ⅰ(GTP cyclohydrolase 1,GCH1)相互作用,并通过了体内和体外的蛋白质结合实验证实了这两个蛋白质之间的特异性相互作用.结论 发现并验证了14-3-3ζ与GCH1之间的蛋白质相互作用,为进一步深入研究这两种蛋白质的功能及所引起的相关疾病的发病机理提供了新的线索.     

Klotho:与FPC蛋白相互作用的蛋白质分子

目的:利用酵母双杂交技术筛选与纤囊素相互作用的蛋白质,为进一步探讨纤囊素(FPC)在常染色体隐性遗传多囊肾病(ARPKD)发生、发展中的作用机制提供依据。方法:利用酵母双杂交系统以质粒pG-BKT7-FPC为"诱饵",在人类胚肾cDNA文库中筛选与FPC蛋白C末端相互作用宿主蛋白的基因,再通过一对一回交试验验证两者之间的相互作用。结果:酵母双杂交筛选得到相互作用的蛋白分子Klotho(后简称KL),回交试验再次确认KL能够与FPC蛋白相互作用。结论:FPC的C末端能够与KL相互作用,它们之间的相互作用可能为研究FPC在ARPKD发病中的功能及作用机制提供新途径。     

骨质疏松症蛋白质相互作用网络的构建和分子复合物通路预测

背景：在蛋白质相互作用网络的基础上研究骨质疏松症,可以更深入全面了解其发生发展机制。 目的：建立基于骨质疏松症遗传相关基因的蛋白质相互作用网络,对其中所含分子复合物涉及的生物学通路进行预测。 方法：筛选在线人类孟德尔遗传数据库中的骨质疏松症遗传相关基因,应用Cytoscape软件和插件Agilent Literature Search,进行文本挖掘并建立骨质疏松症的蛋白质相互作用网络;应用插件Clusterviz,发现网络中的可能包含的分子复合物;基于DAVID,富集分子复合物的生物学通路。 结果与结论：人类孟德尔遗传数据库中骨质疏松遗传相关基因有177个。骨质疏松症的蛋白质相互作用网络包含863个节点(蛋白质)、2 925条边(相互作用关系),可能包含4个分子复合物。其中分子复合物3涉及免疫细胞表面分子及其相互黏附、细胞因子与其受体的结合、造血和止血功能等生物学通路;分子复合物4与糖尿病的发生有一定关系。     

蛋白质-蛋白质相互作用数据库的研究进展

随着人类蛋白质组学的飞速发展,出现了大量的蛋白质相互作用及其网络数据,因此寻找和预测蛋白质相互作用的生物信息学方法成了研究蛋白质互作必不可少的工具之一,本文主要从生物信息学角度对蛋白质相互作用数据库做一下简单的介绍,包括各数据库的来源、主要功能、应用范围、目前的发展状况及将来的发展方向等方面.     

Inferring domain-domain interactions from protein-protein interactions

The interaction between proteins is one of the most important features of protein functions. Behind protein-protein interactions there are protein domains interacting physically with one another to perform the necessary functions. Therefore, understanding protein interactions at the domain level gives a global view of the protein interaction network, and possibly of protein functions. Two research groups used yeast two-hybrid assays to generate 5719 interactions between proteins of the yeast Saccharomyces cerevisiae. This allows us to study the large-scale conserved patterns of interactions between protein domains. Using evolutionarily conserved domains defined in a protein-domain database called PFAM (http://PFAM.wustl.edu), we apply a Maximum Likelihood Estimation method to infer interacting domains that are consistent with the observed protein-protein interactions. We estimate the probabilities of interactions between every pair of domains and measure the accuracies of our predictions at the protein level. Using the inferred domain-domain interactions, we predict interactions between proteins. Our predicted protein-protein interactions have a significant overlap with the protein-protein interactions (MIPS: http://mips.gfs.de) obtained by methods other than the two-hybrid assays. The mean correlation coefficient of the gene expression profiles for our predicted interaction pairs is significantly higher than that for random pairs. Our method has shown robustness in analyzing incomplete data sets and dealing with various experimental errors. We found several novel protein-protein interactions such as RPS0A interacting with APG17 and TAF40 interacting with SPT3, which are consistent with the functions of the proteins.     

Insight into bacterial virulence mechanisms against host immune response via the Yersinia pestis-human protein-protein interaction network

A Yersinia pestis-human protein interaction network is reported here to improve our understanding of its pathogenesis. Up to 204 interactions between 66 Y. pestis bait proteins and 109 human proteins were identified by yeast two-hybrid assay and then combined with 23 previously published interactions to construct a protein-protein interaction network. Topological analysis of the interaction network revealed that human proteins targeted by Y. pestis were significantly enriched in the proteins that are central in the human protein-protein interaction network. Analysis of this network showed that signaling pathways important for host immune responses were preferentially targeted by Y. pestis, including the pathways involved in focal adhesion, regulation of cytoskeleton, leukocyte transendoepithelial migration, and Toll-like receptor (TLR) and mitogen-activated protein kinase (MAPK) signaling. Cellular pathways targeted by Y. pestis are highly relevant to its pathogenesis. Interactions with host proteins involved in focal adhesion and cytoskeketon regulation pathways could account for resistance of Y. pestis to phagocytosis. Interference with TLR and MAPK signaling pathways by Y. pestis reflects common characteristics of pathogen-host interaction that bacterial pathogens have evolved to evade host innate immune response by interacting with proteins in those signaling pathways. Interestingly, a large portion of human proteins interacting with Y. pestis (16/109) also interacted with viral proteins (Epstein-Barr virus [EBV] and hepatitis C virus [HCV]), suggesting that viral and bacterial pathogens attack common cellular functions to facilitate infections. In addition, we identified vasodilator-stimulated phosphoprotein (VASP) as a novel interaction partner of YpkA and showed that YpkA could inhibit in vitro actin assembly mediated by VASP.     

PreSPI: design and implementation of protein-protein interaction prediction service system

With the recognition of the importance of computational approach for protein-protein interaction prediction, many techniques have been developed to computationally predict protein-protein interactions. However, few techniques are actually implemented and announced in service form for general users to readily access and use the techniques. In this paper, we design and implement a protein interaction prediction service system based on the domain combination based protein-protein interaction prediction technique, which is known to show superior accuracy to other conventional computational protein-protein interaction prediction methods. In the prediction accuracy test of the method, high sensitivity (77%) and specificity (95%) are achieved for test protein pairs containing common domains with learning sets of proteins in a Yeast. The stability of the method is also manifested through the testing over DIP CORE, HMS-PCI, and TAP data. The functions of the system are divided into core, subsidiary, and general service function categories. The core function category includes the functions that can be provided only by using the domain combination based protein-protein interaction prediction method. Interaction prediction for a single protein pair and visualization of interaction probability distributions are the functions in this category. The subsidiary function category includes the functions that can be derived from the core functions. Domain combination pair search with high appearance probability and construction of protein interaction network are the functions in this category. Lastly, the general service function category includes the functions that can be implemented by collecting and organizing the protein and domain data in the Internet. Performance, openness and flexibility are the major design goals and they are achieved by adopting parallel execution techniques, Web Services standards, and layered architecture respectively. In this paper, several representative user interfaces of the system are also introduced with comprehensive usage guides.     

Functional proteomics mapping of a human signaling pathway

Access to the human genome facilitates extensive functional proteomics studies. Here, we present an integrated approach combining large-scale protein interaction mapping, exploration of the interaction network, and cellular functional assays performed on newly identified proteins involved in a human signaling pathway. As a proof of principle, we studied the Smad signaling system, which is regulated by members of the transforming growth factor beta (TGFbeta) superfamily. We used two-hybrid screening to map Smad signaling protein-protein interactions and to establish a network of 755 interactions, involving 591 proteins, 179 of which were poorly or not annotated. The exploration of such complex interaction databases is improved by the use of PIMRider, a dedicated navigation tool accessible through the Web. The biological meaning of this network is illustrated by the presence of 18 known Smad-associated proteins. Functional assays performed in mammalian cells including siRNA knock-down experiments identified eight novel proteins involved in Smad signaling, thus validating this integrated functional proteomics approach.     

Overrepresentation of genetic variation in the AnkyrinG interactome is related to a range of neurodevelopmental disorders

Upon the discovery of numerous genes involved in the pathogenesis of neurodevelopmental disorders, several studies showed that a significant proportion of these genes converge on common pathways and protein networks. Here, we used a reversed approach, by screening the AnkyrinG protein-protein interaction network for genetic variation in a large cohort of 1009 cases with neurodevelopmental disorders. We identified a significant enrichment of de novo potentially disease-causing variants in this network, confirming that this protein network plays an important role in the emergence of several neurodevelopmental disorders.Subject terms: Genetics research, Neurological disorders     

Predicting protein complex membership using probabilistic network reliability

Evidence for specific protein-protein interactions is increasingly available from both small- and large-scale studies, and can be viewed as a network. It has previously been noted that errors are frequent among large-scale studies, and that error frequency depends on the large-scale method used. Despite knowledge of the error-prone nature of interaction evidence, edges (connections) in this network are typically viewed as either present or absent. However, use of a probabilistic network that considers quantity and quality of supporting evidence should improve inference derived from protein networks. Here we demonstrate inference of membership in a partially known protein complex by using a probabilistic network model and an algorithm previously used to evaluate reliability in communication networks.     

规模化蛋白质相互作用的研究技术

蛋白质相互作用既是蛋白质执行功能的主要方式,也是细胞功能调控网络的结构基础.蛋白质间异常的相互作用及其连锁网络的紊乱是引起许多病理改变的原因.作为功能基因组和蛋白质组研究的重要内容,规模化蛋白质相互作用研究已成为近年国际上研究的热点之一.文章综述了当前规模化蛋白质相互作用研究中的常用技术和常用蛋白质相互作用数据库,研究者可根据研究需要和技术特点利用这些资源.