Literature DB >> 12469341

Interaction and domain networks of yeast.

Stefan Wuchty1.   

Abstract

Data of currently available protein-protein interaction sets and protein domain sets of yeast are used to set up protein and domain interaction and domain sequence networks. All of them are far from being random or regular networks. In fact, they turn out to be sparse and locally well clustered indicating so-called scale-free and partially small-world topology. These subtle topologies display considerable indirect properties which are measured with a newly introduced transitivity coefficient. Fairly small sets of highly connected proteins and domains shape the topologies of the underlying networks, emphasizing a kind of backbone the nets are based on. The biological nature of these particular nodes is further investigated. Since highly connected proteins and domains accumulated a significant higher number of links by their important involvement in certain cellular aspects, their mutational effect on the cell is considered by a perturbation analysis. In comparison to domains of yeast, what factors force domains to accumulate links to other domains in protein sequences of higher eukaryotes are investigated.

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Year:  2002        PMID: 12469341     DOI: 10.1002/1615-9861(200212)2:12<1715::AID-PROT1715>3.0.CO;2-O

Source DB:  PubMed          Journal:  Proteomics        ISSN: 1615-9853            Impact factor:   3.984


  17 in total

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Authors:  Michael Strong; Thomas G Graeber; Morgan Beeby; Matteo Pellegrini; Michael J Thompson; Todd O Yeates; David Eisenberg
Journal:  Nucleic Acids Res       Date:  2003-12-15       Impact factor: 16.971

2.  Evolution and topology in the yeast protein interaction network.

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Journal:  Genome Res       Date:  2004-07       Impact factor: 9.043

3.  Multimeric threading-based prediction of protein-protein interactions on a genomic scale: application to the Saccharomyces cerevisiae proteome.

Authors:  Long Lu; Adrian K Arakaki; Hui Lu; Jeffrey Skolnick
Journal:  Genome Res       Date:  2003-06       Impact factor: 9.043

Review 4.  Three independent determinants of protein evolutionary rate.

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Journal:  J Mol Evol       Date:  2013-02-12       Impact factor: 2.395

5.  Shadows of complexity: what biological networks reveal about epistasis and pleiotropy.

Authors:  Anna L Tyler; Folkert W Asselbergs; Scott M Williams; Jason H Moore
Journal:  Bioessays       Date:  2009-02       Impact factor: 4.345

6.  Transcript profiling of common bean (Phaseolus vulgaris L.) using the GeneChip Soybean Genome Array: optimizing analysis by masking biased probes.

Authors:  S Samuel Yang; Oswaldo Valdés-López; Wayne W Xu; Bruna Bucciarelli; John W Gronwald; Georgina Hernández; Carroll P Vance
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7.  Stable evolutionary signal in a yeast protein interaction network.

Authors:  Stefan Wuchty; Albert-Laszlo Barabási; Michael T Ferdig
Journal:  BMC Evol Biol       Date:  2006-01-30       Impact factor: 3.260

8.  Why do hubs tend to be essential in protein networks?

Authors:  Xionglei He; Jianzhi Zhang
Journal:  PLoS Genet       Date:  2006-04-26       Impact factor: 5.917

9.  Centrality analysis methods for biological networks and their application to gene regulatory networks.

Authors:  Dirk Koschützki; Falk Schreiber
Journal:  Gene Regul Syst Bio       Date:  2008-05-15

10.  Effect of dataset selection on the topological interpretation of protein interaction networks.

Authors:  Luke Hakes; David L Robertson; Stephen G Oliver
Journal:  BMC Genomics       Date:  2005-09-20       Impact factor: 3.969

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