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.     

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.     

基于酵母基因组高通量数据的基因-基因相互作用预测

后基因组时代的显著特点是大规模基因组和蛋白质组实验平台所产生的大量高通量数据,整合并利用基因组和蛋白组信息成为这一时代的主要挑战之一. 因此,基因-基因相互作用将有助于理解细胞内基因之间的相互作用以及信号传导通路研究提供有价值的参考. 为预测酵母基因组中基因-基因相互作用,我们利用高通量数据中的蛋白-蛋白相互作用、遗传表型数据、基因微阵列表达数据以及功能基因注释数据等来分析酵母中的基因-基因相互作用. 本文建立的预测方法为在系统水平上理解酵母基因组中的基因功能提供了依据,也为揭示酵母基因组中的基因-基因相互作用网络奠定理论基础.     

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.     

Proteomics to study genes and genomes

Proteomics, the large-scale analysis of proteins, will contribute greatly to our understanding of gene function in the post-genomic era. Proteomics can be divided into three main areas: (1) protein micro-characterization for large-scale identification of proteins and their post-translational modifications; (2) 'differential display' proteomics for comparison of protein levels with potential application in a wide range of diseases; and (3) studies of protein-protein interactions using techniques such as mass spectrometry or the yeast two-hybrid system. Because it is often difficult to predict the function of a protein based on homology to other proteins or even their three-dimensional structure, determination of components of a protein complex or of a cellular structure is central in functional analysis. This aspect of proteomic studies is perhaps the area of greatest promise. After the revolution in molecular biology exemplified by the ease of cloning by DNA methods, proteomics will add to our understanding of the biochemistry of proteins, processes and pathways for years to come.     

From genomics to proteomics

Proteomics is the study of the function of all expressed proteins. Tremendous progress has been made in the past few years in generating large-scale data sets for protein-protein interactions, organelle composition, protein activity patterns and protein profiles in cancer patients. But further technological improvements, organization of international proteomics projects and open access to results are needed for proteomics to fulfil its potential.     

酵母双杂交系统的研究进展

酵母双杂交系统自创建以来,已成为研究蛋白质相互作用的重要手段,揭示了大量未知蛋白质之间的相互作用。随着该系统的广泛应用,近年来又发展了三杂交系统、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,蛋白质与DNA、RNA、配体之间相互作用的研究。     

Visualization of transient encounter complexes in protein-protein association

Kinetic data on a number of protein-protein associations have provided evidence for the initial formation of a pre-equilibrium encounter complex that subsequently relaxes to the final stereospecific complex. Site-directed mutagenesis and brownian dynamics simulations have suggested that the rate of association can be modulated by perturbations in charge distribution outside the direct interaction surfaces. Furthermore, rate enhancement through non-specific binding may occur by either a reduction in dimensionality or the presence of a short-range, non-specific attractive potential. Here, using paramagnetic relaxation enhancement, we directly demonstrate the existence and visualize the distribution of an ensemble of transient, non-specific encounter complexes under equilibrium conditions for a relatively weak protein-protein complex between the amino-terminal domain of enzyme I and the phosphocarrier protein HPr. Neither the stereospecific complex alone nor any single alternative conformation can account fully for the intermolecular paramagnetic relaxation enhancement data. Restrained rigid-body simulated annealing refinement against the paramagnetic relaxation enhancement data enables us to obtain an atomic probability distribution map of the non-specific encounter complex ensemble that qualitatively correlates with the electrostatic surface potentials on the interacting proteins. Qualitatively similar results are presented for two other protein-protein complexes.     

Evidence for dynamically organized modularity in the yeast protein-protein interaction network

In apparently scale-free protein-protein interaction networks, or 'interactome' networks, most proteins interact with few partners, whereas a small but significant proportion of proteins, the 'hubs', interact with many partners. Both biological and non-biological scale-free networks are particularly resistant to random node removal but are extremely sensitive to the targeted removal of hubs. A link between the potential scale-free topology of interactome networks and genetic robustness seems to exist, because knockouts of yeast genes encoding hubs are approximately threefold more likely to confer lethality than those of non-hubs. Here we investigate how hubs might contribute to robustness and other cellular properties for protein-protein interactions dynamically regulated both in time and in space. We uncovered two types of hub: 'party' hubs, which interact with most of their partners simultaneously, and 'date' hubs, which bind their different partners at different times or locations. Both in silico studies of network connectivity and genetic interactions described in vivo support a model of organized modularity in which date hubs organize the proteome, connecting biological processes--or modules--to each other, whereas party hubs function inside modules.     

A combined algorithm for genome-wide prediction of protein function

The availability of over 20 fully sequenced genomes has driven the development of new methods to find protein function and interactions. Here we group proteins by correlated evolution, correlated messenger RNA expression patterns and patterns of domain fusion to determine functional relationships among the 6,217 proteins of the yeast Saccharomyces cerevisiae. Using these methods, we discover over 93,000 pairwise links between functionally related yeast proteins. Links between characterized and uncharacterized proteins allow a general function to be assigned to more than half of the 2,557 previously uncharacterized yeast proteins. Examples of functional links are given for a protein family of previously unknown function, a protein whose human homologues are implicated in colon cancer and the yeast prion Sup35.     

Comparative assessment of large-scale data sets of protein-protein interactions

Comprehensive protein protein interaction maps promise to reveal many aspects of the complex regulatory network underlying cellular function. Recently, large-scale approaches have predicted many new protein interactions in yeast. To measure their accuracy and potential as well as to identify biases, strengths and weaknesses, we compare the methods with each other and with a reference set of previously reported protein interactions.     

The Plasmodium protein network diverges from those of other eukaryotes

Plasmodium falciparum is the pathogen responsible for over 90% of human deaths from malaria. Therefore, it has been the focus of a considerable research initiative, involving the complete DNA sequencing of the genome, large-scale expression analyses, and protein characterization of its life-cycle stages. The Plasmodium genome sequence is relatively distant from those of most other eukaryotes, with more than 60% of the 5,334 encoded proteins lacking any notable sequence similarity to other organisms. To systematically elucidate functional relationships among these proteins, a large two-hybrid study has recently mapped a network of 2,846 interactions involving 1,312 proteins within Plasmodium. This network adds to a growing collection of available interaction maps for a number of different organisms, and raises questions about whether the divergence of Plasmodium at the sequence level is reflected in the configuration of its protein network. Here we examine the degree of conservation between the Plasmodium protein network and those of model organisms. Although we find 29 highly connected protein complexes specific to the network of the pathogen, we find very little conservation with complexes observed in other organisms (three in yeast, none in the others). Overall, the patterns of protein interaction in Plasmodium, like its genome sequence, set it apart from other species.     

双分子荧光互补技术及其在蛋白互作研究中的应用

双分子荧光互补（bimolecula fluorescence complementation,BiFC）是近年新发展起来的在活细胞中研究蛋白质互作及其定位的新技术．该技术巧妙的将荧光蛋白切成不发光的两个片段,并与目的蛋白融合表达．如果目的蛋白相互作用,连接其上的荧光互补蛋白片段将会相互靠近并恢复荧光活性,从而实现了分子与细胞事件的可视化．BiFC既可以验证蛋白之间的互作、检测其互作强弱,还可以定位互作蛋白的位点．基于双分子荧光蛋白的应用已经越来越广泛,本文对双分子荧光互补技术的原理、应用现状、特点及其面临的问题进行了概述．     

基于混合核函数SVM的蛋白质相互作用预测方法

针对蛋白质相互作用的预测问题,提出一种以余弦核和线性差值累加核为基核的对偶混合核函数SVM的蛋白质相互作用预测方法.该方法充分考虑了蛋白质的结构域特征,同时根据蛋白质相互作用数据应具有顺序无关的特点,将"对偶"思想引入SVM核函数中.对两个真实的蛋白质相互作用数据集Yeast PPI和Human PPI的测试结果表明,提出的方法与其它方法相比能够有效地提高蛋白质相互作用预测的准确率.