Linked Life Data

12782323

Source:http://linkedlifedata.com/resource/pubmed/id/12782323

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1-3
pubmed:dateCreated
2003-6-3
pubmed:abstractText
Protein-protein interactions are facilitated by a myriad of residue-residue contacts on the interacting proteins. Identifying the site of interaction in the protein is a key for deciphering its functional mechanisms, and is crucial for drug development. Many studies indicate that the compositions of contacting residues are unique. Here, we describe a neural network that identifies protein-protein interfaces from sequence. For the most strongly predicted sites (in 34 of 333 proteins), 94% of the predictions were confirmed experimentally. When 70% of our predictions were right, we correctly predicted at least one interaction site in 20% of the complexes (66/333). These results indicate that the prediction of some interaction sites from sequence alone is possible. Incorporating evolutionary and predicted structural information may improve our method. However, even at this early stage, our tool might already assist wet-lab biology.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
0014-5793
pubmed:author
pubmed:issnType
Print
pubmed:day
5
pubmed:volume
544
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
236-9
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
2003
pubmed:articleTitle
Predicted protein-protein interaction sites from local sequence information.
pubmed:affiliation
CUBIC, Department of Biochemistry and Molecular Biophysics, Columbia University, 650 West 168th Street BB217, New York, NY 10032, USA. ofran@cubic.bioc.columbia.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S.