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

Abstract

We describe a novel system, GRIFFIN (G-protein and Receptor Interaction Feature Finding INstrument), that predicts 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 our 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). GRIFFIN (http://griffin.cbrc.jp/) is freely available and allows users to easily execute this reliable prediction of G-proteins.

Figure 1

A flowchart of the integrated system for predicting GPCR–G-protein coupling selectivity.

Figure 2

(a) The top of the GRIFFIN website, where the GPCR sequence and ligand molecular weight can be entered. (b) The result page of a GRIFFIN calculation, where the predicted G-proteins of the user-defined sequence are indicated together with physicochemical parameters used in the SVM or HMM calculation.