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Oxford University Press (OUP), Bioinformatics, 5(26), p. 617-624

DOI: 10.1093/bioinformatics/btq008

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Active site prediction using evolutionary and structural information

Journal article published in 2010 by Sriram Sankararaman, Fei Sha, Jack F. Kirsch, Michael I. Jordan ORCID, Kimmen Sjölander
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

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.