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Interaction network

About: Interaction network is a research topic. Over the lifetime, 2700 publications have been published within this topic receiving 113372 citations.


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Journal ArticleDOI
Eli Eisenberg1, Erez Y. Levanon1
TL;DR: Using a cross-genome comparison, it is shown that the older a protein, the better connected it is, and the number of interactions a protein gains during its evolution is proportional to its connectivity.
Abstract: The Saccharomyces cerevisiae protein-protein interaction map, as well as many natural and man-made networks, shares the scale-free topology. The preferential attachment model was suggested as a generic network evolution model that yields this universal topology. However, it is not clear that the model assumptions hold for the protein interaction network. Using a cross-genome comparison, we show that (a) the older a protein, the better connected it is, and (b) the number of interactions a protein gains during its evolution is proportional to its connectivity. Therefore, preferential attachment governs the protein network evolution. Evolutionary mechanisms leading to such preference and some implications are discussed.

234 citations

Journal ArticleDOI
TL;DR: Applying a core decomposition method which allows us to identify the inherent layer structure of the protein interaction network, it is found that the probability of nodes both being essential and evolutionary conserved successively increases toward the innermost cores.
Abstract: A set of highly connected proteins (or hubs) plays an important role for the integrity of the protein interaction network of Saccharomyces cerevisae by connecting the network's intrinsic modules [1, 2]. The importance of the hubs' central placement is further confirmed by their propensity to be lethal. However, although highly emphasized, little is known about the topological coherence among the hubs. Applying a core decomposition method which allows us to identify the inherent layer structure of the protein interaction network, we find that the probability of nodes both being essential and evolutionary conserved successively increases toward the innermost cores. While connectivity alone is often not a sufficient criterion to assess a protein's functional, evolutionary and topological relevance, we classify nodes as globally and locally central depending on their appearance in the inner or outer cores. The observation that globally central proteins participate in a substantial number of protein complexes which display an elevated degree of evolutionary conservation allows us to hypothesize that globally central proteins serve as the evolutionary backbone of the proteome. Even though protein interaction data are extensively flawed, we find that our results are very robust against inaccurately determined protein interactions.

231 citations

Journal ArticleDOI
TL;DR: Agile Protein Interaction DataAnalyzer is an interactive bioinformatics web tool developed to integrate and analyze in a unified and comparative platform main currently known information about protein–protein interactions demonstrated by specific small-scale or large-scale experimental methods.
Abstract: Agile Protein Interaction DataAnalyzer (APID) is an interactive bioinformatics web tool developed to integrate and analyze in a unified and comparative platform main currently known information about protein–protein interactions demonstrated by specific small-scale or large-scale experimental methods. At present, the application includes information coming from five main source databases enclosing an unified sever to explore .35 000 different proteins and 111 000 different proven interactions. The web includes search tools to query and browse upon the data, allowing selection of the interaction pairs based in calculated parameters that weight and qualify the reliability of each given protein interaction. Such parameters are for the ‘proteins’: connectivity, cluster coefficient, Gene Ontology (GO) functional environment, GO environment enrichment; and for the ‘interactions’: number of methods, GO overlapping, iPfam domain–domain interaction. APID also includes a graphic interactive tool to visualize selected sub-networks and to navigate on them or along the whole interaction network. The application is available open access at http://bioinfow.dep.usal. es/apid/.

228 citations

Journal ArticleDOI
TL;DR: The PIPs database provides a new resource on protein–protein interactions in human that is straightforward to browse, or can be exploited completely, for interaction network modelling.
Abstract: The PIPs database (http://www.compbio.dundee.ac.uk/www-pips) is a resource for studying protein–protein interactions in human. It contains predictions of >37 000 high probability interactions of which >34 000 are not reported in the interaction databases HPRD, BIND, DIP or OPHID. The interactions in PIPs were calculated by a Bayesian method that combines information from expression, orthology, domain co-occurrence, post-translational modifications and sub-cellular location. The predictions also take account of the topology of the predicted interaction network. The web interface to PIPs ranks predictions according to their likelihood of interaction broken down by the contribution from each information source and with easy access to the evidence that supports each prediction. Where data exists in OPHID, HPRD, DIP or BIND for a protein pair this is also reported in the output tables returned by a search. A network browser is included to allow convenient browsing of the interaction network for any protein in the database. The PIPs database provides a new resource on protein–protein interactions in human that is straightforward to browse, or can be exploited completely, for interaction network modelling.

227 citations

Journal ArticleDOI
TL;DR: This analysis suggests that the organization of global protein interaction network is highly interconnected and hence interdependent, more like the continuous dense aggregations of stratus clouds than the segregated configuration of altocumulus clouds.
Abstract: Systems biology approaches can reveal intermediary levels of organization between genotype and phenotype that often underlie biological phenomena such as polygenic effects and protein dispensability. An important conceptualization is the module, which is loosely defined as a cohort of proteins that perform a dedicated cellular task. Based on a computational analysis of limited interaction datasets in the budding yeast Saccharomyces cerevisiae, it has been suggested that the global protein interaction network is segregated such that highly connected proteins, called hubs, tend not to link to each other. Moreover, it has been suggested that hubs fall into two distinct classes: “party” hubs are co-expressed and co-localized with their partners, whereas “date” hubs interact with incoherently expressed and diversely localized partners, and thereby cohere disparate parts of the global network. This structure may be compared with altocumulus clouds, i.e., cotton ball–like structures sparsely connected by thin wisps. However, this organization might reflect a small and/or biased sample set of interactions. In a multi-validated high-confidence (HC) interaction network, assembled from all extant S. cerevisiae interaction data, including recently available proteome-wide interaction data and a large set of reliable literature-derived interactions, we find that hub–hub interactions are not suppressed. In fact, the number of interactions a hub has with other hubs is a good predictor of whether a hub protein is essential or not. We find that date hubs are neither required for network tolerance to node deletion, nor do date hubs have distinct biological attributes compared to other hubs. Date and party hubs do not, for example, evolve at different rates. Our analysis suggests that the organization of global protein interaction network is highly interconnected and hence interdependent, more like the continuous dense aggregations of stratus clouds than the segregated configuration of altocumulus clouds. If the network is configured in a stratus format, cross-talk between proteins is potentially a major source of noise. In turn, control of the activity of the most highly connected proteins may be vital. Indeed, we find that a fluctuation in steady-state levels of the most connected proteins is minimized.

224 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202337
202290
2021183
2020221
2019201
2018163