Linked Life Data

17397056

Source:http://linkedlifedata.com/resource/pubmed/id/17397056

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2007-5-28
pubmed:abstractText
Proteins can move freely in three-dimensional space. As a result, their structural properties, such as solvent accessible surface area, backbone dihedral angles, and atomic distances, are continuous variables. However, these properties are often arbitrarily divided into a few classes to facilitate prediction by statistical learning techniques. In this work, we establish an integrated system of neural networks (called Real-SPINE) for real-value prediction and apply the method to predict residue-solvent accessibility and backbone psi dihedral angles of proteins based on information derived from sequences only. Real-SPINE is trained with a large data set of 2640 protein chains, sequence profiles generated from multiple sequence alignment, representative amino-acid properties, a slow learning rate, overfitting protection, and predicted secondary structures. The method optimizes more than 200,000 weights and yields a 10-fold cross-validated Pearson's correlation coefficient (PCC) of 0.74 between predicted and actual solvent accessible surface areas and 0.62 between predicted and actual psi angles. In particular, 90% of 2640 proteins have a PCC value greater than 0.6 between predicted and actual solvent-accessible surface areas. The results of Real-SPINE can be compared with the best reported correlation coefficients of 0.64-0.67 for solvent-accessible surface areas and 0.47 for psi angles. The real-SPINE server, executable programs, and datasets are freely available on http://sparks.informatics.iupui.edu.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jul
pubmed:issn
1097-0134
pubmed:author
pubmed:copyrightInfo
2007 Wiley-Liss, Inc.
pubmed:issnType
Electronic
pubmed:day
1
pubmed:volume
68
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
76-81
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Real-SPINE: an integrated system of neural networks for real-value prediction of protein structural properties.
pubmed:affiliation
Department of Physiology and Biophysics, Howard Hughes Medical Institute Center for Single Molecule Biophysics, State University of New York at Buffalo, Buffalo, New York 14214, USA.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural