Predicting the coupling specificity of GPCRs to G-proteins by support vector machines

Genomics Proteomics Bioinformatics. 2005 Nov;3(4):247-51. doi: 10.1016/s1672-0229(05)03035-4.

Abstract

G-protein coupled receptors (GPCRs) represent one of the most important classes of drug targets for pharmaceutical industry and play important roles in cellular signal transduction. Predicting the coupling specificity of GPCRs to G-proteins is vital for further understanding the mechanism of signal transduction and the function of the receptors within a cell, which can provide new clues for pharmaceutical research and development. In this study, the features of amino acid compositions and physiochemical properties of the full-length GPCR sequences have been analyzed and extracted. Based on these features, classifiers have been developed to predict the coupling specificity of GPCRs to G-proteins using support vector machines. The testing results show that this method could obtain better prediction accuracy.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Amino Acid Sequence
  • Amino Acids / analysis
  • Amino Acids / chemistry
  • Artificial Intelligence*
  • Chemical Phenomena
  • Chemistry, Physical
  • Computational Biology*
  • Databases, Protein
  • GTP-Binding Proteins / chemistry*
  • GTP-Binding Proteins / classification
  • GTP-Binding Proteins / genetics
  • GTP-Binding Proteins / metabolism
  • Humans
  • Protein Structure, Secondary
  • Protein Structure, Tertiary
  • Receptors, G-Protein-Coupled / chemistry*
  • Receptors, G-Protein-Coupled / classification*
  • Receptors, G-Protein-Coupled / genetics
  • Receptors, G-Protein-Coupled / metabolism
  • Sensitivity and Specificity
  • Signal Transduction

Substances

  • Amino Acids
  • Receptors, G-Protein-Coupled
  • GTP-Binding Proteins