Linked Life Data

8592700

Source:http://linkedlifedata.com/resource/pubmed/id/8592700

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
1996-4-4
pubmed:abstractText
An algorithm for the rigid-body superposition of proteins is described and tested. No prior knowledge of equivalent residues is required. To find the common structural core of two proteins, an exhaustive grid search is conducted in three-dimensional angle space, and at each grid point a fast translation search in three-dimensional space is performed. The best superposition at a given angle set is defined by that translation vector which maximizes the weighted number of equivalent C alpha atoms. Filters using the information about the sequential character of the polypeptide chain are employed to identify that rotation and translation which yields the highest topological similarity of the two proteins. The algorithm is shown to find the best superposition of distantly related structures, and to be capable of finding similar structures to a given atomic model in the Brookhaven Protein Data Bank. In a search using granulocyte-macrophage colony-stimulating factor as a template, all other four-helix bundle cytokines with up-up-down-down topology were found to give the highest values of a topological similarity score, followed by interferon-beta and -gamma and those four-helix bundles with the more common up-down-up-down topology. In another example, the insertion domain of the long variant adenylate kinases is demonstrated to share its fold with rubredoxin.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
0887-3585
pubmed:author
pubmed:issnType
Print
pubmed:volume
23
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
187-95
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
1995
pubmed:articleTitle
Structural superposition of proteins with unknown alignment and detection of topological similarity using a six-dimensional search algorithm.
pubmed:affiliation
Fakultät für Biologie, Universität Konstanz, Germany.
pubmed:publicationType
Journal Article, Comparative Study