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PEDS Advance Access published online on August 16, 2004
Protein Engineering Design and Selection, doi:10.1093/protein/gzh061
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Received May 10, 2004
Revised July 23, 2004
Accepted July 26, 2004

Article

Weighted-support vector machines for predicting membrane protein types based on pseudo amino acid composition

Meng Wang 1*, Jie Yang 1, Guo-Ping Liu 1, Zhi-Jie Xu 1, Kuo-Chen Chou 2

1 Institute of Image Processing & Pattern Recognition, Shanghai Jiaotong University, 200030, China
2 Life Science Research Center, Shanghai Jiaotong University, Shanghai 200030, China; Department of Biomedical Engineering, Shanghai Jiaotong University, Shanghai 200030, China; Tianjin Institute of Bioinformatics & Drug Discovery, Tianjin, China; Gordon Life Science Institute, San Diego, CA 92130, USA

* To whom correspondence should be addressed. E-mail: mengking{at}sjtu.edu.cn.


  Abstract

Membrane proteins are generally classified into the following five types: (1) type I membrane protein, (2) type II membrane protein, (3) multipass transmembrane proteins, (4) lipid chain-anchored membrane proteins, and (5) GPI-anchored membrane proteins. Prediction of membrane protein types has become one of the growing hot topics in bioinformatics. Currently, we are facing two critical challenges in this area. One is how to take into account the extremely complicated sequence-order effects; the other is how to deal with the highly uneven sizes of the subsets in a training dataset. In this paper, stimulated by the concept of using the pseudo-amino-acid composition (Chou, K.C.: PROTEINS: Structure, Function, and Genetics, 43: 246-255, 2001; ibid. 2001, 44, 60) to incorporate the sequence-order effects, the spectral analysis technique is introduced to represent the statistical sample of a protein. Based on such a framework, the weighted support vector machine (SVM) algorithm is applied. The new approach has a remarkable power in dealing with the bias caused by the situation when one subset in the training dataset contains much more samples than the other. The new method is particularly useful when our focus is aimed at proteins belonging to small subsets. The results obtained by the self-consistency test, jackknife test, and independent dataset test are quite encouraging, indicating the current approach may serve as a powerful complemental tool to other existing methods for predicting the types of membrane proteins.

Keywords: Pseudo amino acid composition; Membrane Protein Types; Covariant discriminant algorithm; Uneven class size; weighted {nu}-SVM; Spectral analysis; Bioinformatics; Proteomics.
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