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rdf:type |
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lifeskim:mentions |
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pubmed:issue |
5
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pubmed:dateCreated |
2010-2-25
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pubmed:abstractText |
MOTIVATION: The identification of catalytic residues is a key step in understanding the function of enzymes. While a variety of computational methods have been developed for this task, accuracies have remained fairly low. The best existing method exploits information from sequence and structure to achieve a precision (the fraction of predicted catalytic residues that are catalytic) of 18.5% at a corresponding recall (the fraction of catalytic residues identified) of 57% on a standard benchmark. Here we present a new method, Discern, which provides a significant improvement over the state-of-the-art through the use of statistical techniques to derive a model with a small set of features that are jointly predictive of enzyme active sites. RESULTS: In cross-validation experiments on two benchmark datasets from the Catalytic Site Atlas and CATRES resources containing a total of 437 manually curated enzymes spanning 487 SCOP families, Discern increases catalytic site recall between 12% and 20% over methods that combine information from both sequence and structure, and by >or=50% over methods that make use of sequence conservation signal only. Controlled experiments show that Discern's improvement in catalytic residue prediction is derived from the combination of three ingredients: the use of the INTREPID phylogenomic method to extract conservation information; the use of 3D structure data, including features computed for residues that are proximal in the structure; and a statistical regularization procedure to prevent overfitting.
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pubmed:grant |
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pubmed:commentsCorrections |
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http://linkedlifedata.com/resource/pubmed/commentcorrection/20080507-9719646
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pubmed:language |
eng
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pubmed:journal |
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pubmed:citationSubset |
IM
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pubmed:chemical |
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pubmed:status |
MEDLINE
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pubmed:month |
Mar
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pubmed:issn |
1367-4811
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pubmed:author |
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pubmed:issnType |
Electronic
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pubmed:day |
1
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pubmed:volume |
26
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pubmed:owner |
NLM
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pubmed:authorsComplete |
Y
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pubmed:pagination |
617-24
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pubmed:dateRevised |
2010-9-28
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pubmed:meshHeading |
pubmed-meshheading:20080507-Binding Sites,
pubmed-meshheading:20080507-Catalysis,
pubmed-meshheading:20080507-Catalytic Domain,
pubmed-meshheading:20080507-Databases, Protein,
pubmed-meshheading:20080507-Evolution, Molecular,
pubmed-meshheading:20080507-Models, Molecular,
pubmed-meshheading:20080507-Protein Conformation,
pubmed-meshheading:20080507-Protein Folding,
pubmed-meshheading:20080507-Proteins,
pubmed-meshheading:20080507-Proteomics,
pubmed-meshheading:20080507-Sequence Analysis, Protein
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pubmed:year |
2010
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pubmed:articleTitle |
Active site prediction using evolutionary and structural information.
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pubmed:affiliation |
Computer Science Division, University of California, Berkeley, USA.
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pubmed:publicationType |
Journal Article,
Research Support, U.S. Gov't, Non-P.H.S.,
Research Support, N.I.H., Extramural
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