GRIFFIN -- a system for predicting GPCRx96G-protein coupling selectivity using a support vector machine and a hidden Markov model

What you can do:
Predict G-protein coupled receptor (GPCR) and G-protein coupling selectivity based on a support vector machine (SVM) and a hidden Markov model (HMM) with high sensitivity and specificity.
  • Based on the assumption that whole structural segments of ligands, GPCRs and G-proteins are essential to determine GPCR and G-protein coupling, various quantitative features were selected for ligands, GPCRs and G-protein complex structures, and those parameters that are the most effective in selecting G-protein type were used as feature vectors in the SVM.
  • The main part of GRIFFIN includes a hierarchical SVM classifier using the feature vectors, which is useful for Class A GPCRs, the major family.
  • For the opsins and olfactory subfamilies of Class A and other minor families (Classes B, C, frizzled and smoothened), the binding G-protein is predicted with high accuracy using the HMM.
  • Applying this system to known GPCR sequences, each binding G-protein is predicted with high sensitivity and specificity (>85% on average).
  • Heterotrimeric GTP-Binding Proteins
  • G-Protein-Coupled proteins
  • GPCR
  • G-protein coupled receptors
  • GPCR binding prediction tool
  • G-proteins
This record last updated: 09-22-2005
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