酵母双杂交技术的发展

自酵母双杂交系统创建以来，已经成为研究蛋白质相互作用的重要手段，被广泛用于各个生物学研究领域，揭示了大量未知蛋白质之间的相互作用。近年，在经典的酵母双杂交系统基础上，针对其局限性，相继提出许多新的研究方法，如：双（多）筛选法，SOS富集系统，哺乳动物细胞双杂交系统，三杂交系统，单杂交系统，逆向双杂交系统，等等。本将对上述方法进行详细叙述。酵母双杂交及其衍生系统已经成功地运用于蛋白质之间，蛋白质与DNA、PNA、配体之间的相互作用的研究。     

研究蛋白质相互作用的新系统——酵母双杂交系统

自酵母双杂交系统创建以来，已经成为研究蛋白质相互作用的重要手段，被广泛用于各个生物学研究领域，揭示了大量未知蛋白质之间的相互作用，近来，在经典的酵母双杂交系统基础上，又相继提出许多新的研究方法，如：三杂交系统，单杂交系统，逆向双杂交系统，ＳＯＳ富集系统等，酵母双杂交及其衍生系统已经成功地运用于蛋白质之间，蛋白南与ＤＮＡ、ＲＮＡ、配体之间的相互作用的研究。     

酵母双杂交系统的应用与改进

酵母双杂交系统是一种体内研究蛋白质相互作用的非常有效的分子生物学方法,自建立以来得到了不断的改进与发展,并衍生出单杂交系统、三杂交系统、方向杂交系统等一系列相关技术,在蛋白质组学及药物开发研究中发挥了重要作用。     

酵母双杂交系统的改进和发展

酵母双杂交系统是研究蛋白质间相互作用的一种非常有效的分子生物学方法，具有快速和高效的特点，但也存在一些局限性．人们针对它的缺点相应地进行了一些完善和改进，并衍生出单杂交系统、三杂交系统等一系列相关的技术，从而拓宽了该系统的应用领域．     

酵母双杂交技术的应用与发展

酵母双杂交技术是一种有效的真核活细胞内研究方法,在蛋白质相互作用的研究方面得到了广泛的应用并取得了许多有价值的重要发现。作为一个完整的实验系统,它自建立以来经过了不断的改进与完善,不仅进一步提高了实验结果的可靠性与精确性,而且在此基础上又发展了反向双杂交,三杂交及核外双杂交等多项技术。     

ABA受体RCAR1/PYL9相互作用蛋白质的筛选及初步研究

使用拟南芥作为模型,采用酵母双杂交系统筛选出和ABA受体RCAR1/PYL9有相互作用的蛋白质,以更深入的研究RCAR1/PYL9的功能.首先利用反转录PCR方法获得拟南芥的cDNA,然后通过酵母双杂交系统,用拟南芥RCAR1/PYL9作为诱饵蛋白对拟南芥cDNA文库进行筛选,并获得多个阳性克隆.对该菌落进行进一步的鉴定得到与RCAR1/PYL9具有相互作用的蛋白质,这对进一步研究RCAR1/PYL9及其相互作用蛋白质在ABA信号转导途径中的作用及地位奠定了基础.     

应用酵母双杂交系统筛选与NAP1相互作用的蛋白质

用NAP1作为"诱饵"蛋白,通过酵母双杂交系统筛选人淋巴细胞cDNA文库,鉴定阳性克隆;再利用酵母双杂交和免疫共沉淀验证相互作用. 结果表明,通过酵母双杂交系统筛选人淋巴细胞cDNA文库,阳性克隆的鉴定,发现了与NAP1的相互作用蛋白质―蛋白酶体α亚基3(PSMA3). 免疫共沉淀(Co-IP)结果证实外源表达的NAP1蛋白和PSMA3蛋白在293T细胞中有特异性的相互作用. NAP1作为FDC分泌的一种多肽,与PSMA3有特异性的相互作用,这种相互作用如何实现对蛋白酶体降解靶蛋白的活性调节,其作用机理有待深入研究.     

拟南芥ATP6与PSAL蛋白质之间的相互作用研究

本文利用酵母双杂交系统研究了拟南芥中全长atp6与不同长度psaL之间的相互作用,通过观察含有共转化质粒的酵母,在组氨酸(His)和腺嘌呤(Ade)营养缺陷条件下的生长情况,以及对β-半乳糖苷酶(LacZ基因编码)的定性和定量分析,表明ATP6与PSAL之间具有较强的相互作用,并且这种相互作用在全长atp6和psaL的中间区段,PSAL的信号肽和C端的存在阻碍了这种相互作用.     

酵母双杂交系统检测金属硫蛋白(MT)间的相互作用

金属硫蛋白-3(MT-3),又称神经生长抑制因子,为金属硫蛋白家族中惟一具有生物活性的成员。为了验证其在细胞内是否形成二聚或多聚体,构建酵母双杂交系统用于检测。结果表明：MT-3与MT-3之间在酵母细胞内能发生相互作用,进一步用酵母双杂交实验还表明MT-3与MT-1间也存在弱相互作用。由此提示,在机体细胞中,MT-3可以以同源或异源二聚体的形式存在。     

利用酵母双杂交筛选与谷氨酰tRNA合成酶相互作用的蛋白质的初步研究

使用拟南芥作为模型,采用筛选出和谷氨酰tRNA合成酶有相互作用的蛋白质的方法研究谷氨酰tRNA合成酶在植物中可能具有的未知功能.首先利用反转录PCR方法获得拟南芥的cDNA,然后通过酵母双杂交系统,用拟南芥的谷氨酰tRNA合成酶作为诱饵对拟南芥cDNA文库进行筛选,并获得多个阳性菌落.对该菌落进行进一步的鉴定得到和谷氨酰tRNA合成酶具有相互作用的蛋白质,这对进一步研究谷氨酰tRNA合成酶及其相互作用蛋白质参与的生命活动奠定基础.     

The protein-protein interaction map of Helicobacter pylori

With the availability of complete DNA sequences for many prokaryotic and eukaryotic genomes, and soon for the human genome itself, it is important to develop reliable proteome-wide approaches for a better understanding of protein function. As elementary constituents of cellular protein complexes and pathways, protein-protein interactions are key determinants of protein function. Here we have built a large-scale protein-protein interaction map of the human gastric pathogen Helicobacter pylori. We have used a high-throughput strategy of the yeast two-hybrid assay to screen 261 H. pylori proteins against a highly complex library of genome-encoded polypeptides. Over 1,200 interactions were identified between H. pylori proteins, connecting 46.6% of the proteome. The determination of a reliability score for every single protein-protein interaction and the identification of the actual interacting domains permitted the assignment of unannotated proteins to biological pathways.     

Analysis of correlations between protein complex and protein-protein interaction and mRNA expression

Protein-protein interaction is a physical interaction of two proteins in living cells. In budding yeast Saccharomyces cerevisiae, large-seale protein-protein interaction data have been obtained through high-throughput yeast two-hybrid systems (Y2H) and protein complex purification techniques based on mass-spectrometry. Here, we collect 11855 interactions between total 2617 proteins. Through seriate genome-wide mRNA expression data, similarity between two genes could be measured. Protein complex data can also be obtained publicly and can be translated to pair relationship that any two proteins can only exist in the same complex or not. Analysis of protein complex data, protein-protein interaction data and mRNA expression data can elucidate correlations between them. The results show that proteins that have interactions or similar expression patterns have a higher possibility to be in the same protein complex than randomized selected proteins, and proteins which have interactions and similar expression patterns are even more possible to exist in the same protein complex. The work indirates that comprehensive integration and analysis of public large-seale bioinformatical data, such as protein complex data, protein-protein interaction data and mRNA expression data, may help to uncover their relationships and common biological information underlying these data. The strategies described here may help to integrate and analyze other functional genomic and proteomic data, such as gene expression profiling, protein-localization mapping and large-scale phenotypic data, both in yeast and in other organisms.     

A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae

Two large-scale yeast two-hybrid screens were undertaken to identify protein-protein interactions between full-length open reading frames predicted from the Saccharomyces cerevisiae genome sequence. In one approach, we constructed a protein array of about 6,000 yeast transformants, with each transformant expressing one of the open reading frames as a fusion to an activation domain. This array was screened by a simple and automated procedure for 192 yeast proteins, with positive responses identified by their positions in the array. In a second approach, we pooled cells expressing one of about 6,000 activation domain fusions to generate a library. We used a high-throughput screening procedure to screen nearly all of the 6,000 predicted yeast proteins, expressed as Gal4 DNA-binding domain fusion proteins, against the library, and characterized positives by sequence analysis. These approaches resulted in the detection of 957 putative interactions involving 1,004 S. cerevisiae proteins. These data reveal interactions that place functionally unclassified proteins in a biological context, interactions between proteins involved in the same biological function, and interactions that link biological functions together into larger cellular processes. The results of these screens are shown here.     

Screening of the interacting proteins with NifA in <Emphasis Type="Italic">Azospirillum brasilense</Emphasis> Sp7 by the yeast two-hybrid system

NifA in AzospiriUum brasilense plays a key role in regulating the synthesis and activity of nitrogenase in response to ammonia and oxygen available. In this work we used the yeast two-hybrid system to identify the proteins that interact with NifA. The nifA gene was fused to the yeast two-hybrid vector pGBD-C2, and three A. brasilense Sp7 genomic libraries for use in yeast two-hybrid studies were constructed. Screening of the libraries identified four clones encoding proteins that interact with NifA. The confirmation of the interactions of each gene product of the four clones and NifA were carried out by exchanging the vectors for nifA and the four clones and by mutageneses of the four clones with shift reading frame experiments in yeast two-hybrid studies. DNA sequence analyses showed that two clones encode proteins containing PAS domains that play an important role in signal transduction. One clone has high similarity with the fhuE gene of Escherichia coli, whose gene product is involved in iron uptake and transportation, and the other clone encodes an unknown protein.     

Towards a proteome-scale map of the human protein-protein interaction network

Systematic mapping of protein-protein interactions, or 'interactome' mapping, was initiated in model organisms, starting with defined biological processes and then expanding to the scale of the proteome. Although far from complete, such maps have revealed global topological and dynamic features of interactome networks that relate to known biological properties, suggesting that a human interactome map will provide insight into development and disease mechanisms at a systems level. Here we describe an initial version of a proteome-scale map of human binary protein-protein interactions. Using a stringent, high-throughput yeast two-hybrid system, we tested pairwise interactions among the products of approximately 8,100 currently available Gateway-cloned open reading frames and detected approximately 2,800 interactions. This data set, called CCSB-HI1, has a verification rate of approximately 78% as revealed by an independent co-affinity purification assay, and correlates significantly with other biological attributes. The CCSB-HI1 data set increases by approximately 70% the set of available binary interactions within the tested space and reveals more than 300 new connections to over 100 disease-associated proteins. This work represents an important step towards a systematic and comprehensive human interactome project.     

Protein interaction maps for complete genomes based on gene fusion events

A large-scale effort to measure, detect and analyse protein-protein interactions using experimental methods is under way. These include biochemistry such as co-immunoprecipitation or crosslinking, molecular biology such as the two-hybrid system or phage display, and genetics such as unlinked noncomplementing mutant detection. Using the two-hybrid system, an international effort to analyse the complete yeast genome is in progress. Evidently, all these approaches are tedious, labour intensive and inaccurate. From a computational perspective, the question is how can we predict that two proteins interact from structure or sequence alone. Here we present a method that identifies gene-fusion events in complete genomes, solely based on sequence comparison. Because there must be selective pressure for certain genes to be fused over the course of evolution, we are able to predict functional associations of proteins. We show that 215 genes or proteins in the complete genomes of Escherichia coli, Haemophilus influenzae and Methanococcus jannaschii are involved in 64 unique fusion events. The approach is general, and can be applied even to genes of unknown function.     

Global landscape of protein complexes in the yeast Saccharomyces cerevisiae

Identification of protein-protein interactions often provides insight into protein function, and many cellular processes are performed by stable protein complexes. We used tandem affinity purification to process 4,562 different tagged proteins of the yeast Saccharomyces cerevisiae. Each preparation was analysed by both matrix-assisted laser desorption/ionization-time of flight mass spectrometry and liquid chromatography tandem mass spectrometry to increase coverage and accuracy. Machine learning was used to integrate the mass spectrometry scores and assign probabilities to the protein-protein interactions. Among 4,087 different proteins identified with high confidence by mass spectrometry from 2,357 successful purifications, our core data set (median precision of 0.69) comprises 7,123 protein-protein interactions involving 2,708 proteins. A Markov clustering algorithm organized these interactions into 547 protein complexes averaging 4.9 subunits per complex, about half of them absent from the MIPS database, as well as 429 additional interactions between pairs of complexes. The data (all of which are available online) will help future studies on individual proteins as well as functional genomics and systems biology.