| pubmed-article:20080507 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:20080507 | lifeskim:mentions | umls-concept:C0205681 | lld:lifeskim |
| pubmed-article:20080507 | lifeskim:mentions | umls-concept:C0681842 | lld:lifeskim |
| pubmed-article:20080507 | lifeskim:mentions | umls-concept:C0678594 | lld:lifeskim |
| pubmed-article:20080507 | lifeskim:mentions | umls-concept:C1533716 | lld:lifeskim |
| pubmed-article:20080507 | pubmed:issue | 5 | lld:pubmed |
| pubmed-article:20080507 | pubmed:dateCreated | 2010-2-25 | lld:pubmed |
| pubmed-article:20080507 | 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. | lld:pubmed |
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| pubmed-article:20080507 | pubmed:language | eng | lld:pubmed |
| pubmed-article:20080507 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:20080507 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:20080507 | pubmed:chemical | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:20080507 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:20080507 | pubmed:month | Mar | lld:pubmed |
| pubmed-article:20080507 | pubmed:issn | 1367-4811 | lld:pubmed |
| pubmed-article:20080507 | pubmed:author | pubmed-author:KirschJack... | lld:pubmed |
| pubmed-article:20080507 | pubmed:author | pubmed-author:JordanMichael... | lld:pubmed |
| pubmed-article:20080507 | pubmed:author | pubmed-author:SjölanderKimm... | lld:pubmed |
| pubmed-article:20080507 | pubmed:author | pubmed-author:ShaFeiF | lld:pubmed |
| pubmed-article:20080507 | pubmed:author | pubmed-author:SankararamanS... | lld:pubmed |
| pubmed-article:20080507 | pubmed:issnType | Electronic | lld:pubmed |
| pubmed-article:20080507 | pubmed:day | 1 | lld:pubmed |
| pubmed-article:20080507 | pubmed:volume | 26 | lld:pubmed |
| pubmed-article:20080507 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:20080507 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:20080507 | pubmed:pagination | 617-24 | lld:pubmed |
| pubmed-article:20080507 | pubmed:dateRevised | 2010-9-28 | lld:pubmed |
| pubmed-article:20080507 | pubmed:meshHeading | pubmed-meshheading:20080507... | lld:pubmed |
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| pubmed-article:20080507 | pubmed:meshHeading | pubmed-meshheading:20080507... | lld:pubmed |
| pubmed-article:20080507 | pubmed:meshHeading | pubmed-meshheading:20080507... | lld:pubmed |
| pubmed-article:20080507 | pubmed:year | 2010 | lld:pubmed |
| pubmed-article:20080507 | pubmed:articleTitle | Active site prediction using evolutionary and structural information. | lld:pubmed |
| pubmed-article:20080507 | pubmed:affiliation | Computer Science Division, University of California, Berkeley, USA. | lld:pubmed |
| pubmed-article:20080507 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:20080507 | pubmed:publicationType | Research Support, U.S. Gov't, Non-P.H.S. | lld:pubmed |
| pubmed-article:20080507 | pubmed:publicationType | Research Support, N.I.H., Extramural | lld:pubmed |
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