Literature DB >> 15190252

Evidence for dynamically organized modularity in the yeast protein-protein interaction network.

Jing-Dong J Han1, Nicolas Bertin, Tong Hao, Debra S Goldberg, Gabriel F Berriz, Lan V Zhang, Denis Dupuy, Albertha J M Walhout, Michael E Cusick, Frederick P Roth, Marc Vidal.   

Abstract

In apparently scale-free protein-protein interaction networks, or 'interactome' networks, most proteins interact with few partners, whereas a small but significant proportion of proteins, the 'hubs', interact with many partners. Both biological and non-biological scale-free networks are particularly resistant to random node removal but are extremely sensitive to the targeted removal of hubs. A link between the potential scale-free topology of interactome networks and genetic robustness seems to exist, because knockouts of yeast genes encoding hubs are approximately threefold more likely to confer lethality than those of non-hubs. Here we investigate how hubs might contribute to robustness and other cellular properties for protein-protein interactions dynamically regulated both in time and in space. We uncovered two types of hub: 'party' hubs, which interact with most of their partners simultaneously, and 'date' hubs, which bind their different partners at different times or locations. Both in silico studies of network connectivity and genetic interactions described in vivo support a model of organized modularity in which date hubs organize the proteome, connecting biological processes--or modules--to each other, whereas party hubs function inside modules.

Entities:  

Mesh:

Substances:

Year:  2004        PMID: 15190252     DOI: 10.1038/nature02555

Source DB:  PubMed          Journal:  Nature        ISSN: 0028-0836            Impact factor:   49.962


  598 in total

1.  Deciphering the Arabidopsis floral transition process by integrating a protein-protein interaction network and gene expression data.

Authors:  Fei He; Yuan Zhou; Ziding Zhang
Journal:  Plant Physiol       Date:  2010-06-07       Impact factor: 8.340

2.  Systematic control of protein interactions for systems biology.

Authors:  Nitin Bhardwaj; Declan Clarke; Mark Gerstein
Journal:  Proc Natl Acad Sci U S A       Date:  2011-12-12       Impact factor: 11.205

Review 3.  Identification of aberrant pathways and network activities from high-throughput data.

Authors:  Jinlian Wang; Yuji Zhang; Catalin Marian; Habtom W Ressom
Journal:  Brief Bioinform       Date:  2012-01-27       Impact factor: 11.622

Review 4.  Proteome-wide prediction of protein-protein interactions from high-throughput data.

Authors:  Zhi-Ping Liu; Luonan Chen
Journal:  Protein Cell       Date:  2012-06-22       Impact factor: 14.870

5.  The structural and energetic basis for high selectivity in a high-affinity protein-protein interaction.

Authors:  Nicola A G Meenan; Amit Sharma; Sarel J Fleishman; Colin J Macdonald; Bertrand Morel; Ruth Boetzel; Geoffrey R Moore; David Baker; Colin Kleanthous
Journal:  Proc Natl Acad Sci U S A       Date:  2010-05-17       Impact factor: 11.205

6.  Functional dissection of an intrinsically disordered protein: understanding the roles of different domains of Knr4 protein in protein-protein interactions.

Authors:  Adilia Dagkessamanskaia; Fabien Durand; Vladimir N Uversky; Matteo Binda; Frédéric Lopez; Karim El Azzouzi; Jean Marie Francois; Hélène Martin-Yken
Journal:  Protein Sci       Date:  2010-07       Impact factor: 6.725

7.  Nonspecific binding limits the number of proteins in a cell and shapes their interaction networks.

Authors:  Margaret E Johnson; Gerhard Hummer
Journal:  Proc Natl Acad Sci U S A       Date:  2010-12-27       Impact factor: 11.205

8.  A complex-based reconstruction of the Saccharomyces cerevisiae interactome.

Authors:  Haidong Wang; Boyko Kakaradov; Sean R Collins; Lena Karotki; Dorothea Fiedler; Michael Shales; Kevan M Shokat; Tobias C Walther; Nevan J Krogan; Daphne Koller
Journal:  Mol Cell Proteomics       Date:  2009-01-27       Impact factor: 5.911

Review 9.  Mechanisms of tumor resistance to EGFR-targeted therapies.

Authors:  Elizabeth A Hopper-Borge; Rochelle E Nasto; Vladimir Ratushny; Louis M Weiner; Erica A Golemis; Igor Astsaturov
Journal:  Expert Opin Ther Targets       Date:  2009-03       Impact factor: 6.902

10.  Integrated network analysis identifies fight-club nodes as a class of hubs encompassing key putative switch genes that induce major transcriptome reprogramming during grapevine development.

Authors:  Maria Concetta Palumbo; Sara Zenoni; Marianna Fasoli; Mélanie Massonnet; Lorenzo Farina; Filippo Castiglione; Mario Pezzotti; Paola Paci
Journal:  Plant Cell       Date:  2014-12-09       Impact factor: 11.277

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.