Linked Life Data

21193057

Source:http://linkedlifedata.com/resource/pubmed/id/21193057

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5
pubmed:dateCreated
2011-3-8
pubmed:abstractText
The osmotic stress response signalling pathway of the model yeast Saccharomyces cerevisae is crucial for the survival of cells under osmotic stress, and is preserved to varying degrees in other related fungal species. We apply a method for inference of ancestral states of characteristics over a phylogeny to 17 fungal species to infer the maximum likelihood estimate of presence or absence in ancestral genomes of genes involved in osmotic stress response. The same method allows us furthermore to perform a statistical test for correlated evolution between genes. Where such correlations exist within the osmotic stress response pathway of S. cerevisae, we have used this in order to predict and subsequently test for the presence of physical protein-protein interactions in an attempt to detect novel interactions. Finally we assess the relevance of observed evolutionary correlations in predicting protein interactions in light of the experimental results. We do find that correlated evolution provides some useful information for the prediction of protein-protein interactions, but that these alone are not sufficient to explain detectable patterns of correlated evolution.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
1096-0937
pubmed:author
pubmed:copyrightInfo
Copyright © 2010 Elsevier Inc. All rights reserved.
pubmed:issnType
Electronic
pubmed:volume
48
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
504-11
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
Prediction of putative protein interactions through evolutionary analysis of osmotic stress response in the model yeast Saccharomyces cerevisae.
pubmed:affiliation
Centre for Bioinformatics, Division of Molecular Biosciences, Imperial College London, London SW72AZ, United Kingdom. thomas.thorne@imperial.ac.uk
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't