Probabilistic model of the human protein-protein interaction network
Daniel R. RhodesScott A. TomlinsSooryanarayana VaramballyVasudeva MahavisnoTerrence R. BarretteShanker Kalyana‐SundaramDebashis GhoshAkhilesh PandeyArul M. Chinnaiyan
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Interactome
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Protein-protein interaction network represents an important aspect of systems biology. The understanding of the plant protein-protein interaction network and interactome will provide crucial insights into the regulation of plant developmental, physiological, and pathological processes. In this review, we will first define the concept of plant interactome and the protein-protein interaction network. The significance of the plant interactome study will be discussed. We will then compare the pros and cons for different strategies for interactome mapping including yeast two-hybrid system (Y2H), affinity purification mass spectrometry (AP-MS), bimolecular fluorescence complementation (BiFC), and in silico prediction. The application of these platforms on specific plant biology questions will be further discussed. The recent advancements revealed the great potential for plant protein-protein interaction network and interactome to elucidate molecular mechanisms for signal transduction, stress responses, cell cycle control, pattern formation, and others. Mapping the plant interactome in model species will provide important guideline for the future study of plant biology.
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The rapid development of yeast two-hybrid and immuno-affinity purification make the study of protein-protein interaction in a large scale at the proteome level possible.At present,studies on human protein-protein interaction network in cell,tissue,organ,and even whole body have been doing successively.and the protein number in the protein-protein interaction network have increased from several to whole proteome.The studies of protein-protein interaction network about function,disease and ecology have made some achievements.However,there are also some questions and challenges in the present study.This paper reviews the research methods of the proteinprotein interaction network,the research progress and challenges,and points out the orientation and target of research of the human protein-protein interaction network.
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Human proteome project
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The human network of Protein-Protein Interactions (PPIs) (interactome) provides information on biological systems that can be used to aid prediction of protein function and disease association. As some classes of protein may be the focus of much study, data sets may contain bias, which may affect the results of network analyses. Implicated cancer proteins and proteins including significant known mediators of cardiovascular disease (cvd) display a tendency to play a central role in a previously constructed interactome. However, removing possible bias in the interactome by only considering interactions obtained from non-targeted approaches affects the significance of the findings.
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Human Protein Structural Interaction Network:Domain Effects on Network Topology and Protein Function
Availability of high-throughput protein-protein interaction and domain-domain interaction information make it possible to study human structural interaction network, and uncover the inner relationship between structure and function of proteins in proteomics area.The widely-distributed domains are thought to affect structure and function of proteins.However, it is still a challenge to investigate potential mechanisms of these effects combing the structural information (e.g domain number, domain length and domain coverage).The proteins were classified into single- or multi- domain proteins, then human protein structural interaction network was constructed with classification information by integrating the protein-protein interaction and the domain- domain interaction data.Furthermore, comparing with the protein-protein interaction network, specific structure characteristics of human protein structural interaction network were studied. And with respect to single-/multi- domain proteins, function enrichment analysis was carried out for their functions. With domain-domain interaction considered, human protein structural interaction network could provide more detailed information distinct from networks based on protein-protein interaction datasets only, and might reveal the underlying complexity of protein-protein interaction network from the perspective of protein classification. Human protein structural interaction network could exploit domain information to provide additional and crucial protein interaction details necessary for understanding what human structural interactions imply.
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Helicobacter pylori infections cause gastric ulcers and play a major role in the development of gastric cancer. In 2001, the first protein interactome was published for this species, revealing over 1500 binary protein interactions resulting from 261 yeast two-hybrid screens. Here we roughly double the number of previously published interactions using an ORFeome-based, proteome-wide yeast two-hybrid screening strategy. We identified a total of 1515 protein-protein interactions, of which 1461 are new. The integration of all the interactions reported in H. pylori results in 3004 unique interactions that connect about 70% of its proteome. Excluding interactions of promiscuous proteins we derived from our new data a core network consisting of 908 interactions. We compared our data set to several other bacterial interactomes and experimentally benchmarked the conservation of interactions using 365 protein pairs (interologs) of E. coli of which one third turned out to be conserved in both species.
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While the analysis of unweighted biological webs as diverse as genetic, protein and metabolic networks allowed spectacular insights in the inner workings of a cell, biological networks are not only determined by their static grid of links. In fact, we expect that the heterogeneity in the utilization of connections has a major impact on the organization of cellular activities as well.We consider a web of interactions between protein domains of the Protein Family database (PFAM), which are weighted by a probability score. We apply metrics that combine the static layout and the weights of the underlying interactions. We observe that unweighted measures as well as their weighted counterparts largely share the same trends in the underlying domain interaction network. However, we only find weak signals that weights and the static grid of interactions are connected entities. Therefore assuming that a protein interaction is governed by a single domain interaction, we observe strong and significant correlations of the highest scoring domain interaction and the confidence of protein interactions in the underlying interactions of yeast and fly. Modeling an interaction between proteins if we find a high scoring protein domain interaction we obtain 1, 428 protein interactions among 361 proteins in the human malaria parasite Plasmodium falciparum. Assessing their quality by a logistic regression method we observe that increasing confidence of predicted interactions is accompanied by high scoring domain interactions and elevated levels of functional similarity and evolutionary conservation.Our results indicate that probability scores are randomly distributed, allowing to treat static grid and weights of domain interactions as separate entities. In particular, these finding confirms earlier observations that a protein interaction is a matter of a single interaction event on domain level. As an immediate application, we show a simple way to predict potential protein interactions by utilizing expectation scores of single domain interactions.
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Protein-protein interaction (PPI) network analyses are highly valuable in deciphering and understanding the intricate organisation of cellular functions. Nevertheless, the majority of available protein-protein interaction networks are context-less, i.e. without any reference to the spatial, temporal or physiological conditions in which the interactions may occur. In this work, we are proposing a protocol to infer the most likely protein-protein interaction (PPI) network in human macrophages.We integrated the PPI dataset from the Agile Protein Interaction DataAnalyzer (APID) with different meta-data to infer a contextualized macrophage-specific interactome using a combination of statistical methods. The obtained interactome is enriched in experimentally verified interactions and in proteins involved in macrophage-related biological processes (i.e. immune response activation, regulation of apoptosis). As a case study, we used the contextualized interactome to highlight the cellular processes induced upon Mycobacterium tuberculosis infection.Our work confirms that contextualizing interactomes improves the biological significance of bioinformatic analyses. More specifically, studying such inferred network rather than focusing at the gene expression level only, is informative on the processes involved in the host response. Indeed, important immune features such as apoptosis are solely highlighted when the spotlight is on the protein interaction level.
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Protein-domains play an important role in mediating protein-protein interactions. Furthermore, the same domain-pairs mediate different interactions in different contexts and in various organisms, and therefore domain-pairs are considered as the building blocks of interactome networks. Here we extend these principles to the host-virus interface and find the domain-pairs that potentially mediate human-herpesvirus interactions. Notably, we find that the same domain-pairs used by other organisms for mediating their interactions underlie statistically significant fractions of human-virus protein inter-interaction networks. Our analysis shows that viral domains tend to interact with human domains that are hubs in the human domain-domain interaction network. This may enable the virus to easily interfere with a variety of mechanisms and processes involving various and different human proteins carrying the relevant hub domain. Comparative genomics analysis provides hints at a molecular mechanism by which the virus acquired some of its interacting domains from its human host.
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Abstract Background Protein-protein interactions play a fundamental role in elucidating the molecular mechanisms of biomolecular function, signal transductions and metabolic pathways of living organisms. Although high-throughput technologies such as yeast two-hybrid system and affinity purification followed by mass spectrometry are widely used in model organisms, the progress of protein-protein interactions detection in plants is rather slow. With this motivation, our work presents a computational approach to predict protein-protein interactions in Oryza sativa . Results To better understand the interactions of proteins in Oryza sativa , we have developed PRIN, a Predicted Rice Interactome Network. Protein-protein interaction data of PRIN are based on the interologs of six model organisms where large-scale protein-protein interaction experiments have been applied: yeast ( Saccharomyces cerevisiae ), worm ( Caenorhabditis elegans ), fruit fly ( Drosophila melanogaster ), human ( Homo sapiens ), Escherichia coli K12 and Arabidopsis thaliana . With certain quality controls, altogether we obtained 76,585 non-redundant rice protein interaction pairs among 5,049 rice proteins. Further analysis showed that the topology properties of predicted rice protein interaction network are more similar to yeast than to the other 5 organisms. This may not be surprising as the interologs based on yeast contribute nearly 74% of total interactions. In addition, GO annotation, subcellular localization information and gene expression data are also mapped to our network for validation. Finally, a user-friendly web interface was developed to offer convenient database search and network visualization. Conclusions PRIN is the first well annotated protein interaction database for the important model plant Oryza sativa . It has greatly extended the current available protein-protein interaction data of rice with a computational approach, which will certainly provide further insights into rice functional genomics and systems biology. PRIN is available online at http://bis.zju.edu.cn/prin/ .
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