Prediction of the types of membrane proteins based on discrete wavelet transform and support vector machines.
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.
Qiu
Jian-Ding
JD
Department of Chemistry and Institute for Advanced Study, Nanchang University, 330031, Nanchang, People's Republic of China. jdqiu@ncu.edu.cn
Sun
Xing-Yu
XY
Huang
Jian-Hua
JH
Liang
Ru-Ping
RP
D000465:Algorithms; D000595:Amino Acid Sequence; D003198:Computer Simulation; D006801:Humans; D008565:Membrane Proteins / Q000737:chemistry* / Q000235:genetics; D008958:Models, Molecular; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated; D011487:Protein Conformation; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid
D008565:Membrane Proteins