Coevolution is a short-distance force at the protein interaction level and correlates with the modular organization of protein networks

We investigated what roles coevolution plays in shaping yeast protein interaction network (PIN). We found that the extent of coevolution between two proteins decreases rapidly as their interacting distance on the PIN increases, suggesting coevolutionary constraint is a short-distance force at the molecular level. We also found that protein-protein interactions (PPIs) with strong coevolution tend to be enriched in interconnected clusters, whereas PPIs with weak coevolution are more frequently present at inter-cluster region. The findings indicate the close relationship between coevolution and modular organization of PINs, and may provide insights into evolution and modularity of cellular networks.     

Computational analysis of human protein interaction networks

Large amounts of human protein interaction data have been produced by experiments and prediction methods. However, the experimental coverage of the human interactome is still low in contrast to predicted data. To gain insight into the value of publicly available human protein network data, we compared predicted datasets, high-throughput results from yeast two-hybrid screens, and literature-curated protein-protein interactions. This evaluation is not only important for further methodological improvements, but also for increasing the confidence in functional hypotheses derived from predictions. Therefore, we assessed the quality and the potential bias of the different datasets using functional similarity based on the Gene Ontology, structural iPfam domain-domain interactions, likelihood ratios, and topological network parameters. This analysis revealed major differences between predicted datasets, but some of them also scored at least as high as the experimental ones regarding multiple quality measures. Therefore, since only small pair wise overlap between most datasets is observed, they may be combined to enlarge the available human interactome data. For this purpose, we additionally studied the influence of protein length on data quality and the number of disease proteins covered by each dataset. We could further demonstrate that protein interactions predicted by more than one method achieve an elevated reliability.     

蛋白质网络聚类算法分析平台的设计与实现

蛋白质网络聚类是识别功能模块的重要手段,不仅有利于理解生物系统的组织结构,对预测蛋白质功能也具有重要的意义。针对目前蛋白质网络聚类算法缺乏有效分析软件的事实,本文设计并实现了一个新的蛋白质网络聚类算法分析平台ClusterE。该平台实现了查全率、查准率、敏感性、特异性、功能富集分析等聚类评估方法,并且集成了FAG-EC、Dpclus、Monet、IPC-MCE、IPCA等聚类算法,不仅可以对蛋白质网络聚类分析结果进行可视化,并且可以在不同聚类分析指标下对多个聚类算法进行可视化比较与分析。该平台具有良好的扩展性,其中聚类算法以及聚类评估方法都是以插件形式集成到系统中。     

Preferential duplication in the sparse part of yeast protein interaction network

Gene duplication is an important mechanism driving the evolution of biomolecular network. Thus, it is expected that there should be a strong relationship between a gene's duplicability and the interactions of its protein product with other proteins in the network. We studied this question in the context of the protein interaction network (PIN) of Saccharomyces cerevisiae. We found that duplicates have, on average, significantly lower clustering coefficient (CC) than singletons, and the proportion of duplicates (PD) decreases steadily with CC. Furthermore, using functional annotation data, we observed a strong negative correlation between PD and the mean CC for functional categories. By partitioning the network into modules and assigning each protein a modularity measure Q(n), we found that CC of a protein is a reflection of its modularity. Moreover, the core components of complexes identified in a recent high-throughput experiment, characterized by high CC, have lower PD than that of the attachments. Subsequently, 2 types of hub were identified by their degree, CC and Q(n). Although PD of intramodular hubs is much less than the network average, PD of intermodular hubs is comparable to, or even higher than, the network average. Our results suggest that high CC, and thus high modularity, pose strong evolutionary constraints on gene duplicability, and gene duplication prefers to happen in the sparse part of PINs.     

Prioritization of orphan disease-causing genes using topological feature and GO similarity between proteins in interaction networks

Identification of disease-causing genes among a large number of candidates is a fundamental challenge in human disease studies. However, it is still time-consuming and laborious to determine the real disease-causing genes by biological experiments. With the advances of the high-throughput techniques, a large number of protein-protein interactions have been produced. Therefore, to address this issue, several methods based on protein interaction network have been proposed. In this paper, we propose a shortest path-based algorithm, named SPranker, to prioritize disease-causing genes in protein interaction networks. Considering the fact that diseases with similar phenotypes are generally caused by functionally related genes, we further propose an improved algorithm SPGOranker by integrating the semantic similarity of GO annotations. SPGOranker not only considers the topological similarity between protein pairs in a protein interaction network but also takes their functional similarity into account. The proposed algorithms SPranker and SPGOranker were applied to 1598 known orphan disease-causing genes from 172 orphan diseases and compared with three state-of-the-art approaches, ICN, VS and RWR. The experimental results show that SPranker and SPGOranker outperform ICN, VS, and RWR for the prioritization of orphan disease-causing genes. Importantly, for the case study of severe combined immunodeficiency, SPranker and SPGOranker predict several novel causal genes.     

The Cartographers toolbox: building bigger and better human protein interaction networks

One of the greatest challenges of the post-genomic era is theconstruction of a more comprehensive human protein interactionmap. While this process may take many years to complete, thedevelopment of stringent high throughput techniques and theemergence of complementary assays mean that the aim of buildinga detailed binary map of the human interactome is now a veryrealistic goal. In particular, methods which facilitate theanalysis of large numbers of membrane-protein interactions meanthat it will be possible to construct more extensive networks,which in turn provide new insights into the functional connectivitybetween intra- and extra-cellular processes. This is importantas many therapeutic strategies are designed to elicit effectsvia ‘tractable’ cell-surface proteins. Therefore,the construction of maps depicting the complexity of trans-cellularcommunication networks will not only improve our understandingof physiological processes, it will also aid the design of rationaltherapeutic strategies, with fewer potential side effects. Thisreview aims to provide a basic insight into the approaches currentlybeing used to construct binary human protein interaction networks,with particular reference to newer techniques, which have thepotential to extend network coverage and aid the conditionalannotation of interactome-scale protein interaction maps.      

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。     

Faster and more accurate global protein function assignment from protein interaction networks using the MFGO algorithm

MOTIVATION: Predicting protein function accurately is an important issue in the post-genomic era. To achieve this goal, several approaches have been proposed deduce the function of unclassified proteins through sequence similarity, co-expression profiles, and other information. Among these methods, the global optimization method (GOM) is an interesting and powerful tool that assigns functions to unclassified proteins based on their positions in a physical interactions network [Vazquez, A., Flammini, A., Maritan, A. and Vespignani, A. (2003) Global protein function prediction from protein-protein interaction networks, Nat. Biotechnol., 21, 697-700]. To boost both the accuracy and speed of GOM, a new prediction method, MFGO (modified and faster global optimization) is presented in this paper, which employs local optimal repetition method to reduce calculation time, and takes account of topological structure information to achieve a more accurate prediction. CONCLUSION: On four proteins interaction datasets, including Vazquez dataset, YP dataset, DIP-core dataset, and SPK dataset, MFGO was tested and compared with the popular MR (majority rule) and GOM methods. Experimental results confirm MFGO's improvement on both speed and accuracy. Especially, MFGO method has a distinctive advantage in accurately predicting functions for proteins with few neighbors. Moreover, the robustness of the approach was validated both in a dataset containing a high percentage of unknown proteins and a disturbed dataset through random insertion and deletion. The analysis shows that a moderate amount of misplaced interactions do not preclude a reliable function assignment.     

Prediction,assessment and validation of protein interaction maps in bacteria

High-throughput proteomics technologies, especially the yeast two-hybrid system, produce large volumes of protein-protein interaction data organized in networks. The complete sequencing of many genomes raises questions about the extent to which such networks can be transferred between organisms. We attempted to answer this question using the experimentally derived Helicobacter pylori interaction map and the recently described interacting domain profile pair (IDPP) method to predict a virtual map for Escherichia coli. The extensive literature concerning E.coli was used to assess all predicted interactions and to validate the IDPP method, which clusters protein domains by sequence and connectivity similarities. The IDPP method has a much better heuristic value than methods solely based on protein homology. The IDPP method was further applied to Campylobacter jejuni to generate a virtual interaction map. An in-depth comparison of the chemotaxis pathways predicted in E.coli and C.jejuni led to the proposition of new functional assignments. Finally, the prediction of protein-protein interaction maps across organisms enabled us to validate some of the interactions on the original experimental map.     

Exploring multiplicity conditions in enzymatic reaction networks

In this work, a novel algorithmic approach to detect multiplicity of steady states in enzymatic reaction networks is presented. The method exploits the structural properties of networks derived from the Chemical Reaction Network Theory. In first instance, the space of parameters is divided in different regions according to the qualitative behavior induced by the parameters in the long term dynamics of the network. Once the regions are identified, a condition for the appearance of multiplicities is checked in the different regions by solving a given optimization problem. In this way, the method allows the characterization of the whole parameter space of biochemical networks in terms of the appearance or not of multistability. The approach is illustrated through a well‐known case of enzymatic catalysis with substrate inhibition. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009     

Match_Motif: A rapid computational tool to assist in protein–protein interaction design

In order to generate protein assemblies with a desired function, the rational design of protein–protein binding interfaces is of significant interest. Approaches based on random mutagenesis or directed evolution may involve complex experimental selection procedures. Also, molecular modeling approaches to design entirely new proteins and interactions with partner molecules can involve large computational efforts and screening steps. In order to simplify at least the initial effort for designing a putative binding interface between two proteins the Match_Motif approach has been developed. It employs the large collection of known protein–protein complex structures to suggest interface modifications that may lead to improved binding for a desired input interaction geometry. The approach extracts interaction motifs based on the backbone structure of short (four residues) segments and the relative arrangement with respect to short segments on the partner protein. The interaction geometry is used to search through a database of such motifs in known stable bound complexes. All matches are rapidly identified (within a few seconds) and collected and can be used to guide changes in the interface that may lead to improved binding. In the output, an alternative interface structure is also proposed based on the frequency of occurrence of side chains at a given interface position in all matches and based on sterical considerations. Applications of the procedure to known complex structures and alternative arrangements are presented and discussed. The program, data files, and example applications can be downloaded from https://www.groups.ph.tum.de/t38/downloads/.     

Functional and topological characterization of protein interaction networks

The elucidation of the cell's large-scale organization is a primary challenge for post-genomic biology, and understanding the structure of protein interaction networks offers an important starting point for such studies. We compare four available databases that approximate the protein interaction network of the yeast, Saccharomyces cerevisiae, aiming to uncover the network's generic large-scale properties and the impact of the proteins' function and cellular localization on the network topology. We show how each database supports a scale-free, topology with hierarchical modularity, indicating that these features represent a robust and generic property of the protein interactions network. We also find strong correlations between the network's structure and the functional role and subcellular localization of its protein constituents, concluding that most functional and/or localization classes appear as relatively segregated subnetworks of the full protein interaction network. The uncovered systematic differences between the four protein interaction databases reflect their relative coverage for different functional and localization classes and provide a guide for their utility in various bioinformatics studies.     

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.     

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.     

NASCENT: An automatic protein interaction network generation tool for non-model organisms

Large quantity of reliable protein interaction data are available for model organisms in public depositories (e.g., MINT, DIP, HPRD, INTERACT). Most data correspond to experiments with the proteins of Saccharomyces cerevisiae, Drosophila melanogaster, Homo sapiens, Caenorhabditis elegans, Escherichia coli and Mus musculus. For other important organisms the data availability is poor or non-existent. Here we present NASCENT, a completely automatic web-based tool and also a downloadable Java program, capable of modeling and generating protein interaction networks even for non-model organisms. The tool performs protein interaction network modeling through gene-name mapping, and outputs the resulting network in graphical form and also in computer-readable graph-forms, directly applicable by popular network modeling software.

Availability

http://nascent.pitgroup.org.     

金黄色葡萄球菌蛋白质相互作用网络及功能

【目的】金黄色葡萄球菌是一种革兰氏阳性菌,是目前最难以对付的病菌之一。它能引起多种感染,特别是在医院环境中。近年来,抗药性金黄色葡萄球菌传染更加严重,已成为公共卫生威胁。由于以前对于金黄色葡萄球菌的实验性研究大都是基于单个基因或者蛋白进行的,为了更好的研究这个物种,有必要从整体上把握金黄色葡萄球菌的蛋白作用机理。【方法】采用系统发生谱、操纵子法、基因融合法、基因邻近法、同源映射法等五种计算方法预测金黄色葡萄球菌蛋白质相互作用网络。【结果】从蛋白组的角度构建了金黄色葡萄球菌蛋白相互作用网络,并对网络进行功能分析。【结论】网络的分析表明金黄色葡萄球菌的蛋白质相互作用网络也服从scale-free属性,发现了SA0939、SA0868、rplD等重要的蛋白。通过对金黄色葡萄球菌的重要的细胞壁合成和信号转导调控蛋白局部网络分析,发现了一些对这两个系统十分重要的蛋白分子,这些信息将为更好的了解金黄色葡萄球菌的致病机理和开发新的药物靶点提供指导。     

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。     

The interaction networks of the budding yeast and human DNA replication-initiation proteins

DNA replication is a stringently regulated cellular process. In proliferating cells, DNA replication-initiation proteins (RIPs) are sequentially loaded onto replication origins during the M-to-G₁ transition to form the pre-replicative complex (pre-RC), a process known as replication licensing. Subsequently, additional RIPs are recruited to form the pre-initiation complex (pre-IC). RIPs and their regulators ensure that chromosomal DNA is replicated exactly once per cell cycle. Origin recognition complex (ORC) binds to, and marks replication origins throughout the cell cycle and recruits other RIPs including Noc3p, Ipi1-3p, Cdt1p, Cdc6p and Mcm2-7p to form the pre-RC. The detailed mechanisms and regulation of the pre-RC and its exact architecture still remain unclear. In this study, pairwise protein-protein interactions among 23 budding yeast and 16 human RIPs were systematically and comprehensively examined by yeast two-hybrid analysis. This study tested 470 pairs of yeast and 196 pairs of human RIPs, from which 113 and 96 positive interactions, respectively, were identified. While many of these interactions were previously reported, some were novel, including various ORC and MCM subunit interactions, ORC self-interactions, and the interactions of IPI3 and NOC3 with several pre-RC and pre-IC proteins. Ten of the novel interactions were further confirmed by co-immunoprecipitation assays. Furthermore, we identified the conserved interaction networks between the yeast and human RIPs. This study provides a foundation and framework for further understanding the architectures, interactions and functions of the yeast and human pre-RC and pre-IC.