Linked Life Data

10089198

Source:http://linkedlifedata.com/resource/pubmed/id/10089198

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
1999-5-24
pubmed:abstractText
A method for recognizing the three-dimensional fold from the protein amino acid sequence based on a combination of hidden Markov models (HMMs) and secondary structure prediction was recently developed for proteins in the Mainly-Alpha structural class. Here, this methodology is extended to Mainly-Beta and Alpha-Beta class proteins. Compared to other fold recognition methods based on HMMs, this approach is novel in that only secondary structure information is used. Each HMM is trained from known secondary structure sequences of proteins having a similar fold. Secondary structure prediction is performed for the amino acid sequence of a query protein. The predicted fold of a query protein is the fold described by the model fitting the predicted sequence the best.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
1367-4803
pubmed:author
pubmed:issnType
Print
pubmed:volume
15
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
131-40
pubmed:dateRevised
2000-12-18
pubmed:meshHeading
pubmed:year
1999
pubmed:articleTitle
FORESST: fold recognition from secondary structure predictions of proteins.
pubmed:affiliation
Analytical Biostatistics Section, Mathematical and Statistical Computing Laboratory, The Institute, Center for Information Technology, National Institutes of Health, Bethesda, MD 20892-5626, USA.
pubmed:publicationType
Journal Article