Active site prediction using evolutionary and structural information.

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.

Results

In cross-validation experiments

SUBMITTER: Sankararaman S 

PROVIDER: S-EPMC2828116 | biostudies-literature | 2010 Mar

REPOSITORIES: biostudies-literature