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 identifi- cation 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 Matthew'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.