The Protein Journal

, Volume 29, Issue 2, pp 114–119 | Cite as

Prediction of the Types of Membrane Proteins Based on Discrete Wavelet Transform and Support Vector Machines

  • Jian-Ding Qiu
  • Xing-Yu Sun
  • Jian-Hua Huang
  • Ru-Ping Liang
Article

Abstract

Membrane proteins are crucial for many biological functions and have become attractive targets for both basic research and drug discovery. With the unprecedented increasing of newly found protein sequences in the post-genomic era, it is both time-consuming and expensive to determine the types of newly found membrane proteins solely with traditional experiment, and so it is highly demanded to develop an automatic method for fast and accurately identifying the type of membrane proteins according to their amino acid sequences. In this study, the discrete wavelet transform (DWT) and support vector machine (SVM) have been used for the prediction of the types of membrane proteins. Maximum accuracy has been obtained using SVM with a wavelet function of bior2.4, a decomposition scale j = 4, and Kyte–Doolittle hydrophobicity scales. The results indicate that the proposed method may play an important complementary role to the existing methods in this area.

Keywords

Membrane proteins Discrete wavelet transform Support vector machine Jackknife test 

Abbreviations

DWT

Discrete wavelet transform

SVM

Support vector machine

KDHΦ

Kyte–Doolittle hydrophobicity

AA

Amino acid

RBF

Radial basis function

Db

Daubechies of number

Bior

Biorthogonal of number

Sym

Symlets of number

DAS

Dense alignment surface

ANN

Artificial nerve network

HMM

Hidden Markov models

PseAA

Pseudo-amino acid composition

TMHMM

HMM based transmembrane (TM)

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Jian-Ding Qiu
    • 1
  • Xing-Yu Sun
    • 1
  • Jian-Hua Huang
    • 1
  • Ru-Ping Liang
    • 1
  1. 1.Department of Chemistry and Institute for Advanced StudyNanchang UniversityNanchangPeople’s Republic of China

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