Fast fourier transform-based support vector machine for prediction of G-protein coupled receptor subfamilies

Acta Biochim Biophys Sin (Shanghai). 2005 Nov;37(11):759-66. doi: 10.1111/j.1745-7270.2005.00110.x.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Amino Acid Sequence
  • Artificial Intelligence*
  • Computer Simulation
  • Fourier Analysis
  • Internet
  • Models, Chemical*
  • Molecular Sequence Data
  • Pattern Recognition, Automated / methods
  • Receptors, G-Protein-Coupled / analysis
  • Receptors, G-Protein-Coupled / chemistry*
  • Receptors, G-Protein-Coupled / classification*
  • Sequence Alignment / methods*
  • Sequence Analysis, Protein / methods*
  • Sequence Homology, Amino Acid

Substances

  • Receptors, G-Protein-Coupled