Linked Life Data

9878345

Source:http://linkedlifedata.com/resource/pubmed/id/9878345

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5
pubmed:dateCreated
1999-3-12
pubmed:abstractText
Topology-representing neural networks are employed to generate pseudo-atomic structures of large-scale protein assemblies by combining high-resolution data with volumetric data at lower resolution. As an application example, actin monomers and structural subdomains are located in a three-dimensional (3D) image reconstruction from electron micrographs. To test the reliability of the method, the resolution of the atomic model of an actin polymer is lowered to a level typically encountered in electron microscopic reconstructions. The atomic model is restored with a precision nine times the nominal resolution of the corresponding low-resolution density. The presented self-organizing computing method may be used as an information-processing tool for the synthesis of structural data from a variety of biophysical sources.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
0022-2836
pubmed:author
pubmed:copyrightInfo
Copyright 1998 Academic Press
pubmed:issnType
Print
pubmed:day
18
pubmed:volume
284
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1247-54
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
1998
pubmed:articleTitle
Self-organizing neural networks bridge the biomolecular resolution gap.
pubmed:affiliation
Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla 92093-0365, USA. wriggers@ucsd.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Research Support, U.S. Gov't, Non-P.H.S., Review, Research Support, Non-U.S. Gov't