Linked Life Data

7497115

Source:http://linkedlifedata.com/resource/pubmed/id/7497115

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
1996-1-18
pubmed:abstractText
The identification of protein sequences that fold into certain known three-dimensional (3D) structures, or motifs, is evaluated through a probabilistic analysis of their one-dimensional (1D) sequences. We present a correlation method that runs in linear time and incorporates pairwise dependencies between amino acid residues at multiple distances to assess the conditional probability that a given residue is part of a given 3D structure. This method is generalized to multiple motifs, where a dynamic programming approach leads to an efficient algorithm that runs in linear time for practical problems. By this approach, we were able to distinguish (2-stranded) coiled-coil from non-coiled-coil domains and globins from nonglobins. When tested on the Brookhaven X-ray crystal structure database, the method does not produce any false-positive or false-negative predictions of coiled coils.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
1066-5277
pubmed:author
pubmed:issnType
Print
pubmed:volume
2
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
125-38
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
1995
pubmed:articleTitle
Algorithms for protein structural motif recognition.
pubmed:affiliation
Mathematics Department, Massachusetts Institute of Technology, Cambridge 02139, USA.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't