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

Yukimitsu Yabuki·Takahiko Muramatsu·Takatsugu Hirokawa·Hidehito Mukai·Makiko Suwa
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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.

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Artificial IntelligenceHeterotrimeric GTP-Binding ProteinsInternetLigandsMarkov ChainsReceptors, G-Protein-CoupledSequence Analysis, ProteinSoftwareJournal ArticleResearch Support, Non-U.S. Gov't

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Yukimitsu Yabuki, Takahiko Muramatsu, Takatsugu Hirokawa, Hidehito Mukai, Makiko Suwa. 2005-07-01. GRIFFIN: a system for predicting GPCR-G-protein coupling selectivity using a support vector machine and a hidden Markov model.. https://doi.org/10.1093/nar%2Fgki495

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