Linked Life Data

17589964

Source:http://linkedlifedata.com/resource/pubmed/id/17589964

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2a
pubmed:dateCreated
2007-6-25
pubmed:abstractText
The success in backbone resonance sequential assignment is fundamental to three dimensional protein structure determination via Nuclear Magnetic Resonance (NMR) spectroscopy. Such a sequential assignment can roughly be partitioned into three separate steps: grouping resonance peaks in multiple spectra into spin systems, chaining the resultant spin systems into strings, and assigning these strings to non-overlapping consecutive amino acid residues in the target protein. Separately dealing with these three steps has been adopted in many existing assignment programs, and it works well on protein NMR data with close-to-ideal quality, while only moderately or even poorly on most real protein datasets, where noises as well as data degeneracies occur frequently. We propose in this work to partition the sequential assignment not by physical steps, but only virtual steps, and use their outputs to cross validate each other. The novelty lies in the places, where the ambiguities at the grouping step will be resolved in finding the highly confident strings at the chaining step, and the ambiguities at the chaining step will be resolved by examining the mappings of strings at the assignment step. In this way, all ambiguities at the sequential assignment will be resolved globally and optimally. The resultant assignment program is called Graph-based Approach for Sequential Assignment (GASA), which has been compared to several recent similar developments including PACES, RANDOM, MARS, and RIBRA. The performance comparisons with these works demonstrated that GASA is more promising for practical use.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
0219-7200
pubmed:author
pubmed:issnType
Print
pubmed:volume
5
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
313-33
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
GASA: a graph-based automated NMR backbone resonance sequential assignment program.
pubmed:affiliation
Department of Computing Science, University of Alberta, Edmonton, Alberta T6G 2E8, Canada. xiangwan@cs.ualberta.ca
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't