根据基因表达谱筛选间接互作蛋白质进行蛋白质功能预测

利用基因表达谱数据,通过计算互作蛋白质的表达相关系数,来筛选、优化蛋白质互作网络。结果显示,利用经过筛选的互作数据,根据邻居计数法和卡方法进行功能预测的预测效果明显提高,距离待测蛋白质较远的邻居也包含着与待测蛋白质功能一致的信息。     

结合蛋白质互作与基因表达谱信息大范围预测蛋白质的精细功能

GESTs(gene expression similarity and taxonomy similarity)是结合基因表达相似性和基因功能分类体系Gene Ontology (GO)中的功能概念相似性测度进行功能预测的新方法. 将此预测算法推广应用于蛋白质互相作用数据, 并提出了几种在蛋白质互作网络中为功能待测蛋白质筛选邻居的方法. 与已有的其它蛋白质功能预测方法不同, 新方法在学习过程中自动地从功能分类体系中的各个功能类中选择最合适的尽可能具体细致的功能类, 利用注释于其相近功能类中的互作邻居蛋白质支持对此具体功能类的预测. 使用MIPS提供的酵母蛋白质互作信息与一套基因表达谱数据, 利用特别针对GO体系结构层次特点设计的3种测度, 评价对GO知识体系中的生物过程分支进行蛋白质功能预测的效果. 结果显示, 利用文中的方法, 可以大范围预测蛋白质的精细功能. 此外, 还利用此方法对2004年底Gene Ontology上未知功能的蛋白质进行预测, 其中部分预测结果在2006年4月发布的SGD注释数据中已经得到了证实.     

人类蛋白质互作网络hub蛋白与其结构域关联分析

hub蛋白质作为参与较多互作的“中心蛋白”,在实现蛋白质功能和生命活动中发挥着关键作用．而结构域作为蛋白质上的基本功能区域,决定着蛋白质功能及蛋白质互作的情况．互作网络中hub蛋白质和结构域对于蛋白质功能的实现均起到决定性的作用．对蛋白质互作与结构域的关系分析表明,蛋白质互作与结构域之间存在着密切的联系．对人类蛋白质互作网络中的hub蛋白与结构域进行关联分析,探讨hub蛋白及其互作partner与结构域数目之间的关系．并通过hub蛋白质之间的互作对相应结构域的关系进行进一步的论证．     

人类蛋白质结构互作网络——结构域对网络拓扑与蛋白质功能的影响

高通量的蛋白质互作数据与结构域互作数据的出现,使得在蛋白质组学领域内研究人类蛋白质结构互作网络,进一步揭示蛋白质结构与功能间的潜在关系成为可能.蛋白质上广泛分布的结构域被认为是蛋白质结构、功能以及进化的基本功能单元.然而,结合蛋白质的结构信息(例如蛋白质结构域数目、长度和覆盖率等)来研究这些表象后的内部机制仍然面临着挑战.将蛋白质分为单结构域蛋白质与多结构域蛋白质,并进一步结合蛋白质互作信息与结构域互作信息构建了人类蛋白质结构互作网络;通过与人类蛋白质互作网络进行比较,研究了人类蛋白质结构互作网络的特殊结构特征;对于单结构域蛋白质与多结构域蛋白质,分别进行了功能富集分析、功能离散度分析以及功能一致性分析等.结果发现,将结构域互作信息综合考虑进来后,人类蛋白质结构互作网络可以提供更多的单纯的蛋白质互作网络无法提供的细节信息,揭示蛋白质互作网络的复杂性.     

根据蛋白质互作网络预测前列腺癌相关蛋白质的细致功能

对于功能部分已知的前列腺癌相关蛋白质,提出了一种基于Gent Ontology的功能特异的子网构建方法来细化其功能注释。结果显示该方法能够以很高的精确率为前列腺癌相关蛋白质预测更为精细的功能。预测的相关蛋白质的功能对于指导实验研究前列腺癌的分子机制具有重要的价值。     

核酸－蛋白质互作的生物化学研究方法

研究核酸－蛋白质的互作是揭示生命活动机理的基础, 文章简要综述了用于研究核酸－蛋白质互作的各种生物化学方法。从体内、体外两个研究角度, 针对核酸、蛋白以及复合物3个研究水平, 概述了硝化纤维膜过滤实验、足迹法、EMSA、Southwestern杂交等经典分析方法的原理、发展和运用。还着重介绍了最近在表观遗传学领域中广泛运用的nChIP、xChIP等基本染色质免疫沉淀(ChIP)技术及其衍生出的ChIP-on-chip等方法。     

线粒体和细胞核的互作

线粒体是一个半自主的细胞器,它有自己的基因组,能进行ＤＮＡ的复制、转录和翻译,可以编码自身的ｒＲＮＡ、ｔＲＮＡ以及少量蛋白质。但这些过程并不是线粒体完全独立地进行的,它离不开核基因组的指导与调控。线粒体基因表达所必需的一些蛋白质,如ＲＮＡ聚合酶、核糖体大亚单位以及许多调控因子都是由核基因编码,在细胞质的核糖体上合成后,运输进线粒体后再起作用。线粒体功能的正常发挥需要线粒体基因组和核基因组的互作。组成呼吸链的一系列结构蛋白是由线粒体和细胞核共同编码的,这些蛋白质的正确组装,受核基因的控制。同时,研…     

类蛋白质互作网络hub蛋白与其结构域关联分析

hub蛋白质作为参与较多互作的"中心蛋白".在实现蛋白质功能和生命活动中发挥着关键作用.而结构域作为蛋白质上的基本功能区域,决定着蛋白质功能及蛋白质互作的情况.互作网络中hub蛋白质和结构域对于蛋白质功能的实现均起到决定性的作用.对蛋白质互作与结构域的关系分析表明.蛋白质互作与结构域之间存在着密切的联系.对人类蛋白质互作网络中的hub蛋白与结构域进行关联分析.探讨hub蛋白及其互作partner与结构域数目之间的关系,并通过hub蛋白质之间的互作对相应结构域的关系进行进一步的论证.     

根据蛋白质互作网络预测乳腺癌相关蛋白质的细致功能

乳腺癌是最为常见的恶性肿瘤之一。已有的关于乳腺癌相关蛋白质的功能注释比较宽泛, 制约了乳腺癌的后续研究工作。对于已知部分功能的乳腺癌相关蛋白质, 提出了一种结合Gene Ontology功能先验知识和蛋白质互作的方法, 通过构建功能特异的局部相互作用网络来预测乳腺癌相关蛋白质的细致功能。结果显示该方法能够以很高的精确率为乳腺癌相关蛋白质预测更为精细的功能。预测的相关蛋白质的功能对于指导实验研究乳腺癌的分子机制具有重要的价值。     

基于蛋白质互作知识的生物学通路扩充新方法

生物学通路被广泛应用于基因功能学研究, 但现有的生物学通路知识并不完善, 仍需进一步扩充。生物信息学预测为通路扩充提供了一种有效且经济的途径。文章提出了一种融合蛋白质-蛋白质互作知识以及Gene Ontology(GO)数据库信息进行基因通路预测的新方法。首先选取目标基因在蛋白质-蛋白质互作层面上的邻居所在的Kyoto Encyclopedia of Genes and Genomes(KEGG)通路为候选通路, 然后通过检验候选通路中的基因是否在与目标基因关联的GO节点富集来判断目标基因的通路归属。分别利用Human Protein Reference Database (HPRD)和Biological General Repository for Interaction Datasets(BioGRID)数据库中的蛋白质-蛋白质互作信息进行预测。结果表明, 在两套数据中, 随着互作邻居个数的增加, 预测的平均准确率(在所有目标基因注释的通路中被成功预测的比例)及相对准确率(在至少有一个注释通路被成功预测的基因集中, 所有注释通路均被预测正确的基因所占的比例)均呈现上升趋势。当互作邻居个数达到22时, 预测的平均准确率分别达到96.2%(HPRD)和96.3%(BioGRID), 而相对准确率分别为93.3%(HPRD)和84.1%(BioGRID)。进一步利用新版数据库对旧版数据库中被更新的89个基因进行验证, 至少有一个更新通路被预测正确的基因有50个, 其中43个基因的更新通路被完全正确预测, 相对准确率为86.0%。这些结果显示该方法是一种可靠且有效的通路扩充方法。     

Detecting temporal protein complexes from dynamic protein-protein interaction networks

Background

Proteins dynamically interact with each other to perform their biological functions. The dynamic operations of protein interaction networks (PPI) are also reflected in the dynamic formations of protein complexes. Existing protein complex detection algorithms usually overlook the inherent temporal nature of protein interactions within PPI networks. Systematically analyzing the temporal protein complexes can not only improve the accuracy of protein complex detection, but also strengthen our biological knowledge on the dynamic protein assembly processes for cellular organization.

Results

In this study, we propose a novel computational method to predict temporal protein complexes. Particularly, we first construct a series of dynamic PPI networks by joint analysis of time-course gene expression data and protein interaction data. Then a Time Smooth Overlapping Complex Detection model (TS-OCD) has been proposed to detect temporal protein complexes from these dynamic PPI networks. TS-OCD can naturally capture the smoothness of networks between consecutive time points and detect overlapping protein complexes at each time point. Finally, a nonnegative matrix factorization based algorithm is introduced to merge those very similar temporal complexes across different time points.

Conclusions

Extensive experimental results demonstrate the proposed method is very effective in detecting temporal protein complexes than the state-of-the-art complex detection techniques.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-335) contains supplementary material, which is available to authorized users.     

Widely predicting specific protein functions based on protein-protein interaction data and gene expression profile

GESTs (gene expression similarity and taxonomy similarity), a gene functional prediction approach previously proposed by us, is based on gene expression similarity and concept similarity of functional classes defined in Gene Ontology (GO). In this paper, we extend this method to protein-protein interaction data by introducing several methods to filter the neighbors in protein interaction networks for a protein of unknown function(s). Unlike other conventional methods, the proposed approach automatically selects the most appropriate functional classes as specific as possible during the learning process, and calls on genes annotated to nearby classes to support the predictions to some small-sized specific classes in GO. Based on the yeast protein-protein interaction information from MIPS and a dataset of gene expression profiles, we assess the performances of our approach for predicting protein functions to “biology process” by three measures particularly designed for functional classes organized in GO. Results show that our method is powerful for widely predicting gene functions with very specific functional terms. Based on the GO database published in December 2004, we predict some proteins whose functions were unknown at that time, and some of the predictions have been confirmed by the new SGD annotation data published in April, 2006.     

Widely predicting specific protein functions based on protein-protein interaction data and gene expression profile

GESTs (gene expression similarity and taxonomy similarity), a gene functional prediction approach previously proposed by us, is based on gene expression similarity and concept similarity of functional classes defined in Gene Ontology (GO). In this paper, we extend this method to protein-protein interac-tion data by introducing several methods to filter the neighbors in protein interaction networks for a protein of unknown function(s). Unlike other conventional methods, the proposed approach automati-cally selects the most appropriate functional classes as specific as possible during the learning proc-ess, and calls on genes annotated to nearby classes to support the predictions to some small-sized specific classes in GO. Based on the yeast protein-protein interaction information from MIPS and a dataset of gene expression profiles, we assess the performances of our approach for predicting protein functions to “biology process” by three measures particularly designed for functional classes organ-ized in GO. Results show that our method is powerful for widely predicting gene functions with very specific functional terms. Based on the GO database published in December 2004, we predict some proteins whose functions were unknown at that time, and some of the predictions have been confirmed by the new SGD annotation data published in April, 2006.     

Measuring and analyzing tissue specificity of human genes and protein complexes

Proteins and their interactions are essential for the survival of each human cell. Knowledge of their tissue occurrence is important for understanding biological processes. Therefore, we analyzed microarray and high-throughput RNA-sequencing data to identify tissue-specific and universally expressed genes. Gene expression data were used to investigate the presence of proteins, protein interactions and protein complexes in different tissues. Our comparison shows that the detection of tissue-specific genes and proteins strongly depends on the applied measurement technique. We found that microarrays are less sensitive for low expressed genes than high-throughput sequencing. Functional analyses based on microarray data are thus biased towards high expressed genes. This also means that previous biological findings based on microarrays might have to be re-examined using high-throughput sequencing results.     

利用基因共表达网络分析蛋白质的相互作用

基于Hom in的基因共表达网络的比较分析,发现人类基因共表达网络和蛋白质相互作用数据之间存在一定的相关性。采用基因本体论对这两个网络重叠区域进行基因分类后发现,这些编码的蛋白质主要集中在对刺激物的应答途径之中。通过对该途径中的蛋白质相互作用网络作图,获得了两个独立的功能模块。通过对模块中的基因分类和关键基因分析得出两者分别对应于内外源刺激物的应答功能。本研究对于利用不断丰富的核酸公共数据信息挖掘蛋白质相互作用的研究具有积极的促进作用。     

Unveiling network-based functional features through integration of gene expression into protein networks

Decoding health and disease phenotypes is one of the fundamental objectives in biomedicine. Whereas high-throughput omics approaches are available, it is evident that any single omics approach might not be adequate to capture the complexity of phenotypes. Therefore, integrated multi-omics approaches have been used to unravel genotype–phenotype relationships such as global regulatory mechanisms and complex metabolic networks in different eukaryotic organisms. Some of the progress and challenges associated with integrated omics studies have been reviewed previously in comprehensive studies. In this work, we highlight and review the progress, challenges and advantages associated with emerging approaches, integrating gene expression and protein-protein interaction networks to unravel network-based functional features. This includes identifying disease related genes, gene prioritization, clustering protein interactions, developing the modules, extract active subnetworks and static protein complexes or dynamic/temporal protein complexes. We also discuss how these approaches contribute to our understanding of the biology of complex traits and diseases. This article is part of a Special Issue entitled: Cardiac adaptations to obesity, diabetes and insulin resistance, edited by Professors Jan F.C. Glatz, Jason R.B. Dyck and Christine Des Rosiers.     

Specific species and tissue differences for the gene expression of calcium-binding protein regucalcin

The existence and expression of gene encoding the Ca²⁺-binding protein regucalcin in various species and tissues were investigated with Southern and Northern hybridization analyses using regucalcin cDNA (0.9 kb of open reading frame). Genomic Southern hybridization analysis demonstrated that regucalcin gene was widely conserved among higher animals including human, monkey, rat, mouse, dog, bovine, rabbit and chicken. The gene was not found in yeast. The Northern blot analysis of poly (A)⁺RNAs extracted from the liver of various species showed that regucalcin mRNA was predominantly expressed in rat and mouse, although the expression was also seen in human, bovine and chicken. Furthermore, the enzyme-linked immunoadsorbent assay (ELISA) with rabbit-anti-regucalcin IgG indicated that hepatic regucalcin concentration was most pronounced in rat as compared with that of guinea pig, mouse and chicken. These observations show that the gene expression of regucalcin and its protein synthesis is unique in the liver of rats, suggesting the existence of a specific mechanism in demonstrating regucalcin synthesis from gene.     

Potential target gene and protein interaction ofPimpinella brachycarpa HD-Zip protein, Phz4 by yeast two-hybrid system

The yeast two-hybrid system was used to investigate dimerization between proteins ofPhz2 andPhz4 clones of the homeodomain-leucine zipper family which were obtained by screening aPimpinella brachycarpa shoot-tip cDNA library. Assays showed that Phz4 formed a homo rather than a heterodimer with Phz2. In addition, we isolated cDNA clones,Phyb1, Phyb2, andPhyb3, that encode proteins interacting with Phz4. Although Phyb1 is not a HD-Zip protein, the activity of interaction between Phyb1 and Phz4 was, surprisingly, stronger than that of the homodimerization of Phz4. The analysis of interacting parts indicated that from 1 bp to 466 bp of Phyb1, there was no interaction with Phz4, but from 467 bp to 593 bp, interactions were found with the N-terminal and C-terminal regions, except for HD-Zip of Phz4. This region ofPhyb1 contained a nuclear localization signal. DNA-binding analysis showed that the Phz4 HD-Zip domain recognized the [T(C/G)ATTG] core sequence and the region containing the [TCATTG] motif, which is, in itself, a promoter in vitro.     

Pairwise interactions of the six human MCM protein subunits

The eukaryotic minichromosome maintenance (MCM) proteins have six subunits, Mcm2 to 7p. Together they play essential roles in the initiation and elongation of DNA replication, and the human MCM proteins present attractive targets for potential anticancer drugs. The six MCM subunits interact and form a ring-shaped heterohexameric complex containing one of each subunit in a variety of eukaryotes, and subcomplexes have also been observed. However, the architecture of the human MCM heterohexameric complex is still unknown. We systematically studied pairwise interactions of individual human MCM subunits by using the yeast two-hybrid system and in vivo protein-protein crosslinking with a non-cleavable crosslinker in human cells followed by co-immunoprecipitation. In the yeast two-hybrid assays, we revealed multiple binary interactions among the six human MCM proteins, and a subset of these interactions was also detected as direct interactions in human cells. Based on our results, we propose a model for the architecture of the human MCM protein heterohexameric complex. We also propose models for the structures of subcomplexes. Thus, this study may serve as a foundation for understanding the overall architecture and function of eukaryotic MCM protein complexes and as clues for developing anticancer drugs targeted to the human MCM proteins.