Linked Life Data

15019783

Source:http://linkedlifedata.com/resource/pubmed/id/15019783

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2004-3-15
pubmed:abstractText
An automatic method for recognizing natively disordered regions from amino acid sequence is described and benchmarked against predictors that were assessed at the latest critical assessment of techniques for protein structure prediction (CASP) experiment. The method attains a Wilcoxon score of 90.0, which represents a statistically significant improvement on the methods evaluated on the same targets at CASP. The classifier, DISOPRED2, was used to estimate the frequency of native disorder in several representative genomes from the three kingdoms of life. Putative, long (>30 residue) disordered segments are found to occur in 2.0% of archaean, 4.2% of eubacterial and 33.0% of eukaryotic proteins. The function of proteins with long predicted regions of disorder was investigated using the gene ontology annotations supplied with the Saccharomyces genome database. The analysis of the yeast proteome suggests that proteins containing disorder are often located in the cell nucleus and are involved in the regulation of transcription and cell signalling. The results also indicate that native disorder is associated with the molecular functions of kinase activity and nucleic acid binding.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Mar
pubmed:issn
0022-2836
pubmed:author
pubmed:issnType
Print
pubmed:day
26
pubmed:volume
337
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
635-45
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
Prediction and functional analysis of native disorder in proteins from the three kingdoms of life.
pubmed:affiliation
Bioinformatics Unit, Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't