Linked Life Data

16239729

Source:http://linkedlifedata.com/resource/pubmed/id/16239729

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
Pt 11
pubmed:dateCreated
2005-10-21
pubmed:abstractText
Automating the determination of novel macromolecular structures via X-ray crystallographic methods involves building a model into an electron-density map. Unfortunately, the conventional crystallographic asymmetric unit volumes are usually not well matched to the biological molecular units. In most cases, the facets of the asymmetric unit cut the molecules into a number of disconnected fragments, rendering interpretation by the crystallographer significantly more difficult. The FINDMOL algorithm is designed to quickly parse the arrangement of trace points (pseudo-atoms) derived from a skeletonized electron-density map without requiring higher level prior information such as sequence information or number of molecules in the asymmetric unit. The algorithm was tested with a variety of density-modified maps computed with medium- to low-resolution data. Typically, the resulting volume resembles the biological unit. In the remaining cases the number of disconnected fragments is very small. In all examples, secondary-structural elements such as alpha-helices or beta-sheets are easily identifiable in the defragmented arrangement. FINDMOL can greatly assist a crystallographer during manual model building or in cases where automatic model building can only build partial models owing to limitations of the data such as low resolution and/or poor phases.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Nov
pubmed:issn
0907-4449
pubmed:author
pubmed:issnType
Print
pubmed:volume
61
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1514-20
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
FINDMOL: automated identification of macromolecules in electron-density maps.
pubmed:affiliation
Department of Computer Science, Texas A&M University, 301 H. R. Bright Building, 3112 Texas A&M University, College Station, TX 77843, USA.
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural