Active site prediction using evolutionary and structural information.
BACKGROUND
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.
Sankararaman
Sriram
S
Computer Science Division, University of California, Berkeley, USA.
Sha
Fei
F
Kirsch
Jack F
JF
Jordan
Michael I
MI
Sjölander
Kimmen
K
D001665:Binding Sites; D002384:Catalysis; D020134:Catalytic Domain / Q000235:genetics*; D030562:Databases, Protein; D019143:Evolution, Molecular; D008958:Models, Molecular; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry*; D040901:Proteomics / Q000379:methods*; D020539:Sequence Analysis, Protein
D011506:Proteins