Linked Life Data

21130772

Source:http://linkedlifedata.com/resource/pubmed/id/21130772

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5
pubmed:dateCreated
2011-1-18
pubmed:abstractText
Current homology modeling methods for predicting protein-protein interactions (PPIs) have difficulty in the "twilight zone" (<40%) of sequence identities. Threading methods extend coverage further into the twilight zone by aligning primary sequences for a pair of proteins to a best-fit template complex to predict an entire three-dimensional structure. We introduce a threading approach, iWRAP, which focuses only on the protein interface. Our approach combines a novel linear programming formulation for interface alignment with a boosting classifier for interaction prediction. We demonstrate its efficacy on SCOPPI, a classification of PPIs in the Protein Databank, and on the entire yeast genome. iWRAP provides significantly improved prediction of PPIs and their interfaces in stringent cross-validation on SCOPPI. Furthermore, by combining our predictions with a full-complex threader, we achieve a coverage of 13% for the yeast PPIs, which is close to a 50% increase over previous methods at a higher sensitivity. As an application, we effectively combine iWRAP with genomic data to identify novel cancer-related genes involved in chromatin remodeling, nucleosome organization, and ribonuclear complex assembly. iWRAP is available at http://iwrap.csail.mit.edu.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
1089-8638
pubmed:author
pubmed:copyrightInfo
Copyright © 2010 Elsevier Ltd. All rights reserved.
pubmed:issnType
Electronic
pubmed:day
4
pubmed:volume
405
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1295-310
pubmed:dateRevised
2011-10-6
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
iWRAP: An interface threading approach with application to prediction of cancer-related protein-protein interactions.
pubmed:affiliation
Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA.
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural