Linked Life Data

19359356

Source:http://linkedlifedata.com/resource/pubmed/id/19359356

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
11
pubmed:dateCreated
2009-5-15
pubmed:abstractText
SUMMARY: Sequence-to-structure alignment in template-based protein structure modeling for remote homologs remains a difficult problem even following the correct recognition of folds. Here we present MICAlign, a sequence-to-structure alignment tool that incorporates multiple sources of information from local structural contexts of template, sequence profiles, predicted secondary structures, solvent accessibilities, potential-like terms (including residue-residue contacts and solvent exposures) and pre-aligned structures and sequences. These features, together with a position-specific gap scheme, were integrated into conditional random fields through which the optimal parameters were automatically learned. MICAlign showed improved alignment accuracy over several other state-of-the-art alignment tools based on comparisons by using independent datasets. AVAILABILITY: Freely available at (http://www.bioinfo.tsinghua.edu.cn/~xiaxf/micalign) for both web server and source code.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
1367-4811
pubmed:author
pubmed:issnType
Electronic
pubmed:day
1
pubmed:volume
25
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1433-4
pubmed:dateRevised
2009-11-4
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
MICAlign: a sequence-to-structure alignment tool integrating multiple sources of information in conditional random fields.
pubmed:affiliation
Department of Biological Sciences and Biotechnology, MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Tsinghua University, Beijing, China.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't