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Bioinformatics Advance Access published online on May 3, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl170
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received March 31, 2006
Revised April 26, 2006
Accepted April 27, 2006

Article

Ensemble classifier for protein fold pattern recognition

Hong-Bin Shen 1 and Kuo-Chen Chou 2

1 Institute of Image Processing & Pattern Recognition, Shanghai Jiaotong University, Shanghai 200030, China
2 Institute of Image Processing & Pattern Recognition, Shanghai Jiaotong University, Shanghai 200030, China; Gordon Life Science Institute, San Diego, CA 92130, USA


  Abstract

Motivation: Prediction of protein folding patterns is one level deeper than that of protein structural classes, and hence is much more complicated and difficult. To deal with such a challenging problem, the ensemble classifier was introduced. It was formed by a set of basic classifiers, with each trained in different parameter systems, such as predicted secondary structure, hydrophobicity, van der Waals volume, polarity, polarizability, as well as different dimensions of pseudo amino acid composition, that were extracted from a training dataset. The operation engine for the constituent individual classifiers was OET-KNN (Optimized Evidence-Theoretic K-Nearest Neighbors) rule. Their outcomes were combined thru a weighted voting to give a final determination for classifying a query protein. The recognition was to find the true fold among the 27 possible patterns.

Results: The overall success rate thus obtained was 62% for a testing dataset where most of the proteins have less than 25% sequence identity with the proteins used in training the classifier. Such a rate is 6-21% higher than the corresponding rates obtained by various existing NN (Neural Networks) and SVM (Support Vector Machines) approaches, implying that the ensemble classifier is very promising and might become an useful vehicle in protein science, as well proteomics and bioinformatics.

The ensemble classifier, called PFP-Pred, is available as a web-server at http://www.pami.sjtu.edu.cn/people/hbshen for public usage.

Associate Editor: Keith A Crandall
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