{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,30]],"date-time":"2025-11-30T10:41:58Z","timestamp":1764499318472,"version":"3.37.1"},"reference-count":64,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2016,10,2]],"date-time":"2016-10-02T00:00:00Z","timestamp":1475366400000},"content-version":"vor","delay-in-days":2453,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/2.0\/uk\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2010,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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.<\/jats:p><jats:p>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 \u226550% 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.<\/jats:p><jats:p>Contact: \u00a0kimmen@berkeley.edu<\/jats:p><jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btq008","type":"journal-article","created":{"date-parts":[[2010,1,16]],"date-time":"2010-01-16T01:14:00Z","timestamp":1263604440000},"page":"617-624","source":"Crossref","is-referenced-by-count":64,"title":["Active site prediction using evolutionary and structural information"],"prefix":"10.1093","volume":"26","author":[{"given":"Sriram","family":"Sankararaman","sequence":"first","affiliation":[{"name":"1 Computer Science Division, University of California, Berkeley, 2 Computer Science Department, University of Southern California, 3 Department of Molecular and Cell Biology, 4 Department of Statistics, 5 Department of Bioengineering and 6 Department of Plant and Microbial Biology, University of California, Berkeley, USA"}]},{"given":"Fei","family":"Sha","sequence":"additional","affiliation":[{"name":"1 Computer Science Division, University of California, Berkeley, 2 Computer Science Department, University of Southern California, 3 Department of Molecular and Cell Biology, 4 Department of Statistics, 5 Department of Bioengineering and 6 Department of Plant and Microbial Biology, University of California, Berkeley, USA"}]},{"given":"Jack F.","family":"Kirsch","sequence":"additional","affiliation":[{"name":"1 Computer Science Division, University of California, Berkeley, 2 Computer Science Department, University of Southern California, 3 Department of Molecular and Cell Biology, 4 Department of Statistics, 5 Department of Bioengineering and 6 Department of Plant and Microbial Biology, University of California, Berkeley, USA"}]},{"given":"Michael I.","family":"Jordan","sequence":"additional","affiliation":[{"name":"1 Computer Science Division, University of California, Berkeley, 2 Computer Science Department, University of Southern California, 3 Department of Molecular and Cell Biology, 4 Department of Statistics, 5 Department of Bioengineering and 6 Department of Plant and Microbial Biology, University of California, Berkeley, USA"},{"name":"1 Computer Science Division, University of California, Berkeley, 2 Computer Science Department, University of Southern California, 3 Department of Molecular and Cell Biology, 4 Department of Statistics, 5 Department of Bioengineering and 6 Department of Plant and Microbial Biology, University of California, Berkeley, USA"}]},{"given":"Kimmen","family":"Sj\u00f6lander","sequence":"additional","affiliation":[{"name":"1 Computer Science Division, University of California, Berkeley, 2 Computer Science Department, University of Southern California, 3 Department of Molecular and Cell Biology, 4 Department of Statistics, 5 Department of Bioengineering and 6 Department of Plant and Microbial Biology, University of California, Berkeley, USA"},{"name":"1 Computer Science Division, University of California, Berkeley, 2 Computer Science Department, University of Southern California, 3 Department of Molecular and Cell Biology, 4 Department of Statistics, 5 Department of Bioengineering and 6 Department of Plant and Microbial Biology, University of California, Berkeley, USA"}]}],"member":"286","published-online":{"date-parts":[[2010,1,14]]},"reference":[{"key":"2023012511003467700_B1","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1006\/jmbi.2001.4870","article-title":"Automated structure-based prediction of functional sites in proteins: applications to assessing the validity of inheriting protein function from homology in genome annotation and to protein docking","volume":"311","author":"Aloy","year":"2001","journal-title":"J. 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