Linked Life Data

15751111

Source:http://linkedlifedata.com/resource/pubmed/id/15751111

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2005-3-7
pubmed:abstractText
Protein existing as an ensemble of structures, called intrinsically disordered, has been shown to be responsible for a wide variety of biological functions and to be common in nature. Here we focus on improving sequence-based predictions of long (>30 amino acid residues) regions lacking specific 3-D structure by means of four new neural-network-based Predictors Of Natural Disordered Regions (PONDRs): VL3, VL3H, VL3P, and VL3E. PONDR VL3 used several features from a previously introduced PONDR VL2, but benefitted from optimized predictor models and a slightly larger (152 vs. 145) set of disordered proteins that were cleaned of mislabeling errors found in the smaller set. PONDR VL3H utilized homologues of the disordered proteins in the training stage, while PONDR VL3P used attributes derived from sequence profiles obtained by PSI-BLAST searches. The measure of accuracy was the average between accuracies on disordered and ordered protein regions. By this measure, the 30-fold cross-validation accuracies of VL3, VL3H, and VL3P were, respectively, 83.6 +/- 1.4%, 85.3 +/- 1.4%, and 85.2 +/- 1.5%. By combining VL3H and VL3P, the resulting PONDR VL3E achieved an accuracy of 86.7 +/- 1.4%. This is a significant improvement over our previous PONDRs VLXT (71.6 +/- 1.3%) and VL2 (80.9 +/- 1.4%). The new disorder predictors with the corresponding datasets are freely accessible through the web server at http://www.ist.temple.edu/disprot.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
0219-7200
pubmed:author
pubmed:issnType
Print
pubmed:volume
3
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
35-60
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
Optimizing long intrinsic disorder predictors with protein evolutionary information.
pubmed:affiliation
Center for Information Science and Technology, Temple University, Philadelphia, PA 19122, USA.
pubmed:publicationType
Journal Article, Comparative Study, Research Support, U.S. Gov't, P.H.S., Research Support, Non-U.S. Gov't, Evaluation Studies, Validation Studies