,Fast Fourier Transform-based Support Vector Machine for Prediction of G-protein Coupled Receptor Su

• 作 者: Yan-Zhi GUO Meng-Long LI Ke-Long WANG Zhi-Ning WEN Min-Chun LU Li-Xia LIU Lin JIANG
• 机 构: College of Chemistry, Sichuan University, Chengdu 610064, China
• 出 处: 生物化学与生物物理学报（英文版）
• 发表日期: 2005年11期
• 关键词: G-protein coupled receptor subfamily fast Fourier transform support vector machi

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 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.