Intersectin (ITSN) family of scaffolds function as molecular hubs in protein interaction networks.
Ontology highlight
ABSTRACT:
SUBMITTER: Wong KA
PROVIDER: S-EPMC3338775 | biostudies-literature | 2012
REPOSITORIES: biostudies-literature
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Project description:BackgroundIn the post-genomic era, a central and overarching question in the analysis of protein-protein interaction networks continues to be whether biological characteristics and functions of proteins such as lethality, physiological malfunctions and malignancy are intimately linked to the topological role proteins play in the network as a mathematical structure. One of the key features that have implicitly been presumed is the existence of hubs, highly connected proteins considered to play a crucial role in biological networks. We explore the structure of protein interaction networks of a number of organisms as metric spaces and show that hubs are non randomly positioned and, from a distance point of view, centrally located.ResultsBy analysing how the human functional protein interaction network, the human signalling network, Saccharomyces cerevisiae, Arabidopsis thaliana and Escherichia coli protein-protein interaction networks from various databases are distributed as metric spaces, we found that proteins interact radially through a central node, high degree proteins coagulate in the centre of the network, and those far away from the centre have low degree. We further found that the distribution of proteins from the centre is in some hierarchy of importance and has biological significance.ConclusionsWe conclude that structurally, protein interaction networks are mathematical entities that share properties between organisms but not necessarily with other networks that follow power-law. We therefore conclude that (i) if there are hubs defined by degree, they are not distributed randomly; (ii) zones closest to the centre of the network are enriched for critically important proteins and are also functionally very specialised for specific 'house keeping' functions; (iii) proteins closest to the network centre are functionally less dispensable and may present good targets for therapy development; and (iv) network biology requires its own network theory modelled on actual biological evidence and that simply adopting theories from the social sciences may be misleading.
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Project description:The protein-protein interaction (PPI) networks are dynamically organized as modules, and are typically described by hub dichotomy: 'party' hubs act as intramodule hubs and are coexpressed with their partners, yet 'date' hubs act as coordinators among modules and are incoherently expressed with their partners. However, there remains skepticism about the existence of hub dichotomy. Since different algorithms and data sets were used in previous studies to test the model of hub classification, the conclusions may be largely influenced by the potential inherent biases. In this study, we evaluated two data sets of yeast interactome, and systematically investigated the behavior of hubs from multiple perspectives including co-expression patterns, topological roles and functional classifications. Our results revealed consistency between the two data sets, confirming the presence of hub dichotomy. Furthermore, we analyzed a human interactome data set, and demonstrated that the modular architecture of the PPI networks was more complicated than hub dichotomy.
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Project description:The idea of "date" and "party" hubs has been influential in the study of protein-protein interaction networks. Date hubs display low co-expression with their partners, whilst party hubs have high co-expression. It was proposed that party hubs are local coordinators whereas date hubs are global connectors. Here, we show that the reported importance of date hubs to network connectivity can in fact be attributed to a tiny subset of them. Crucially, these few, extremely central, hubs do not display particularly low expression correlation, undermining the idea of a link between this quantity and hub function. The date/party distinction was originally motivated by an approximately bimodal distribution of hub co-expression; we show that this feature is not always robust to methodological changes. Additionally, topological properties of hubs do not in general correlate with co-expression. However, we find significant correlations between interaction centrality and the functional similarity of the interacting proteins. We suggest that thinking in terms of a date/party dichotomy for hubs in protein interaction networks is not meaningful, and it might be more useful to conceive of roles for protein-protein interactions rather than for individual proteins.
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Project description:UnlabelledHub proteins (those engaged in most physical interactions in a protein interaction network (PIN) have recently gained much research interest due to their essential role in mediating cellular processes and their potential therapeutic value. It is straightforward to identify hubs if the underlying PIN is experimentally determined; however, theoretical hub prediction remains a very challenging task, as physicochemical properties that differentiate hubs from less connected proteins remain mostly uncharacterized. To adequately distinguish hubs from non-hub proteins we have utilized over 1300 protein descriptors, some of which represent QSAR (quantitative structure-activity relationship) parameters, and some reflect sequence-derived characteristics of proteins including domain composition and functional annotations. Those protein descriptors, together with available protein interaction data have been processed by a machine learning method (boosting trees) and resulted in the development of hub classifiers that are capable of predicting highly interacting proteins for four model organisms: Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster and Homo sapiens. More importantly, through the analyses of the most relevant protein descriptors, we are able to demonstrate that hub proteins not only share certain common physicochemical and structural characteristics that make them different from non-hub counterparts, but they also exhibit species-specific characteristics that should be taken into account when analyzing different PINs. The developed prediction models can be used for determining highly interacting proteins in the four studied species to assist future proteomics experiments and PIN analyses.AvailabilityTHE SOURCE CODE AND EXECUTABLE PROGRAM OF THE HUB CLASSIFIER ARE AVAILABLE FOR DOWNLOAD AT: http://www.cnbi2.ca/hub-analysis/
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Project description:MotivationProteins underlay the functioning of a cell and the wiring of proteins in protein-protein interaction network (PIN) relates to their biological functions. Proteins with similar wiring in the PIN (topology around them) have been shown to have similar functions. This property has been successfully exploited for predicting protein functions. Topological similarity is also used to guide network alignment algorithms that find similarly wired proteins between PINs of different species; these similarities are used to transfer annotation across PINs, e.g. from model organisms to human. To refine these functional predictions and annotation transfers, we need to gain insight into the variability of the topology-function relationships. For example, a function may be significantly associated with specific topologies, while another function may be weakly associated with several different topologies. Also, the topology-function relationships may differ between different species.ResultsTo improve our understanding of topology-function relationships and of their conservation among species, we develop a statistical framework that is built upon canonical correlation analysis. Using the graphlet degrees to represent the wiring around proteins in PINs and gene ontology (GO) annotations to describe their functions, our framework: (i) characterizes statistically significant topology-function relationships in a given species, and (ii) uncovers the functions that have conserved topology in PINs of different species, which we term topologically orthologous functions. We apply our framework to PINs of yeast and human, identifying seven biological process and two cellular component GO terms to be topologically orthologous for the two organisms.
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Project description:Hubs are highly connected proteins in a protein-protein interaction network. Previous work has implicated disordered domains and high surface charge as the properties significant in the ability of hubs to bind multiple proteins. While conformational flexibility of disordered domains plays an important role in the binding ability of large hubs, high surface charge is the dominant property in small hubs. In this study, we further investigate the role of the high surface charge in the binding ability of small hubs in the absence of disordered domains. Using multipole expansion, we find that the charges are highly distributed over the hub surfaces. Residue enrichment studies show that the charged residues in hubs are more prevalent on the exposed surface, with the exception of Arg, which is predominantly found at the interface, as compared to non-hubs. This suggests that the charged residues act primarily from the exposed surface rather than the interface to affect the binding ability of small hubs. They do this through (i) enhanced intra-molecular electrostatic interactions to lower the desolvation penalty, (ii) indirect long - range intermolecular interactions with charged residues on the partner proteins for better complementarity and electrostatic steering, and (iii) increased solubility for enhanced diffusion-controlled rate of binding. Along with Arg, we also find a high prevalence of polar residues Tyr, Gln and His and the hydrophobic residue Met at the interfaces of hubs, all of which have the ability to form multiple types of interactions, indicating that the interfaces of hubs are optimized to participate in multiple interactions.
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