Fast fourier transform-based support vector machine for prediction of G-protein coupled receptor subfamilies.
Although the sequence information on G-protein coupled receptors (GPCRs) continues to grow, many GPCRs remain orphaned (i.e. ligand specificity unknown) or poorly characterized with little structural information available, so an automated and reliable method is badly needed to facilitate the identification of novel receptors. In this study, a method of fast Fourier transform-based support vector machine has been developed for predicting GPCR subfamilies according to protein's hydrophobicity. In classifying Class B, C, D and F subfamilies, the method achieved an overall Matthe's correlation coefficient and accuracy of 0.95 and 93.3%, respectively, when evaluated using the jackknife test. The method achieved an accuracy of 100% on the Class B independent dataset. The results show that this method can classify GPCR subfamilies as well as their functional classification with high accuracy. A web server implementing the prediction is available at http://chem.scu.edu.cn/blast/Pred-GPCR.
Guo
Yan-Zhi
YZ
College of Chemistry, Sichuan University, Chengdu 610064, China.
Li
Meng-Long
ML
Wang
Ke-Long
KL
Wen
Zhi-Ning
ZN
Lu
Min-Chun
MC
Liu
Li-Xia
LX
Jiang
Lin
L
D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D003198:Computer Simulation; D005583:Fourier Analysis; D020407:Internet; D008956:Models, Chemical; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods; D043562:Receptors, G-Protein-Coupled / Q000032:analysis / Q000737:chemistry* / Q000145:classification*; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid
D043562:Receptors, G-Protein-Coupled