Bioinformatics Advance Access published online on August 18, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn433
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Accurate sequence-based prediction of catalytic residues
1College of Mathematical Science and LPMC, Nankai University, Tianjin, PRC.
2Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, CANADA.
3Chern Institute of Mathematics, Tianjin, PRC.
*To whom correspondence should be addressed. Dr. Lukasz Kurgan, E-mail: lkurgan{at}ece.ualberta.ca
 |
Abstract |
|---|
Motivation: Prediction of catalytic residues provides useful information for the research on function of enzymes. Most of the existing prediction methods are based on structural information, which limits their use. We propose a sequence-based catalytic residue predictor that provides predictions with quality comparable to modern structure-based methods and that exceeds quality of state-of-the-art se-quence-based methods.
Results: Our method (CRpred) uses sequence-based features and the sequence-derived PSI-BLAST profile. We used feature selection to reduce the dimensionality of the input (and explain the input) to Support Vector Machine (SVM) classifier that provides predictions. Tests on 8 datasets and side-by-side comparison with 6 modern structure- and sequence-based predictors show that CRpred pro-vides predictions with quality comparable to current structure-based methods and better than sequence-based methods. The proposed method obtains 15-19% precision and 48-58% TP rate, depending on the dataset used. CRpred also provides confidence values that allow selecting a subset of predictions with higher precision. The improved quality is due to newly designed features and careful parameterization of the SVM. The features incorporate amino acids characterized by the highest and the lowest propensities to constitute catalytic residues, Gly that provides flexibility for catalytic sites, and sequence motifs characteristic to certain catalytic reactions. Our features indicate that catalytic residues are on average more conserved when compared with the general population of residues and that highly conserved amino acids characterized by high catalytic propensity are likely to form catalytic sites. We also show that local (with respect to the sequence) hydrophobicity contributes towards the prediction.
Availability: http://biomine.ece.ualberta.ca/CRpred/CRpred.htm
Contact: lkurgan{at}ece.ualberta.ca
Associate Editor: Anna Tramontano
Received on May 5, 2008; revised on August 6, 2008; accepted on August 14, 2008
CiteULike 
Connotea 
Del.icio.us What's this?