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Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
Published on: June 6, 2025
Fast fourier transform-based support vector machine for prediction of G-protein coupled receptor subfamilies
Yan-Zhi Guo1, Meng-Long Li, Ke-Long Wang
1College of Chemistry, Sichuan University, Chengdu 610064, China.
Abstract:
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.
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