Linked Life Data

9562545

Source:http://linkedlifedata.com/resource/pubmed/id/9562545

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
1998-7-10
pubmed:abstractText
Distance geometry methods allow protein structures to be constructed using a large number of distance constraints, which can be elucidated by experimental techniques such as NMR. New methods for gleaning tertiary structural information from multiple sequence alignments make it possible for distance constraints to be predicted from sequence information alone. The basic distance geometry method can thus be applied using these empirically derived distance constraints. Such an approach, which incorporates a novel combinatoric procedure, is reported here.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
1359-0278
pubmed:author
pubmed:issnType
Print
pubmed:volume
3
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
149-60
pubmed:dateRevised
2007-11-15
pubmed:meshHeading
pubmed:year
1998
pubmed:articleTitle
A combinatorial distance-constraint approach to predicting protein tertiary models from known secondary structure.
pubmed:affiliation
Computational Chemistry Group, Universitätstrasse 16, ETH Zentrum, Zürich, CH 8092, Switzerland. gareth.chelva@anu.edu.au
pubmed:publicationType
Journal Article, Comparative Study, Research Support, Non-U.S. Gov't