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rdf:type
lifeskim:mentions
pubmed:dateCreated
1997-12-11
pubmed:abstractText
Here we propose an approach to include global structural information in the secondary structure prediction procedure based on hidden Markov models (HMMs) of protein folds. We first identify the correct fold or 'topology' of a protein by means of the HMMs of topology families of proteins. Then the most likely structural model for that protein is used to modify the sequence of secondary structure states previously obtained with a prediction algorithm. Our goal is to investigate the effect on the prediction accuracy of including global structural information in the secondary structure prediction scheme, by means of the HMMs. We find that when the HMM of the predicted topology of a protein is used to adjust the secondary structure sequence predicted originally with the Quadratic-Logistic method, the cross-validated prediction accuracy (Q3) improves by 3%. The topology is correctly predicted in 68% of the cases. We conclude that this HMM based approach is a promising tool for effectively incorporating global structural information in the secondary structure prediction scheme.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1553-0833
pubmed:author
pubmed:issnType
Print
pubmed:volume
5
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
100-3
pubmed:dateRevised
2004-10-11
pubmed:meshHeading
pubmed:year
1997
pubmed:articleTitle
Incorporating global information into secondary structure prediction with hidden Markov models of protein folds.
pubmed:affiliation
National Institutes of Health, Bethesda, MD 20892-5626, USA. valedf@helix.nih.gov
pubmed:publicationType
Journal Article