Linked Life Data

15363931

Source:http://linkedlifedata.com/resource/pubmed/id/15363931

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2004-9-14
pubmed:abstractText
Based on the 639 non-homologous proteins with 2910 cysteine-containing segments of well-resolved three-dimensional structures, a novel approach has been proposed to predict the disulfide-bonding state of cysteines in proteins by constructing a two-stage classifier combining a first global linear discriminator based on their amino acid composition and a second local support vector machine classifier. The overall prediction accuracy of this hybrid classifier for the disulfide-bonding state of cysteines in proteins has scored 84.1% and 80.1%, when measured on cysteine and protein basis using the rigorous jack-knife procedure, respectively. It shows that whether cysteines should form disulfide bonds depends not only on the global structural features of proteins but also on the local sequence environment of proteins. The result demonstrates the applicability of this novel method and provides comparable prediction performance compared with existing methods for the prediction of the oxidation states of cysteines in proteins.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Nov
pubmed:issn
0022-5193
pubmed:author
pubmed:issnType
Print
pubmed:day
7
pubmed:volume
231
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
85-95
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
Cooperativity of the oxidization of cysteines in globular proteins.
pubmed:affiliation
The Key Laboratory of Industrial Biotechnology, Ministry of Education, Southern Yangtze University, 170 Huihe Road, Wuxi 214036, China. sjnbeckham@yahoo.com.cn
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't