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Nucleic Acids Research 2005 33(Web Server Issue):W148-W153; doi:10.1093/nar/gki495
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© The Author 2005. Published by Oxford University Press. All rights reserved
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Article

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

Yukimitsu Yabuki1,2, Takahiko Muramatsu1,3, Takatsugu Hirokawa1, Hidehito Mukai4 and Makiko Suwa1,3,*

1Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST) 2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan 2Information and Mathematical Science Laboratory (IMS) Inc. Meikei Building, 1-5-21 Otsuka, Bunkyo-ku, Tokyo 112-0012, Japan 3Nara Institute of Science and Technology, Graduate School of Information Science 8916-5 Takayama-cho, Ikoma-shi, Nara 630-0192, Japan 4Mitsubishi Kagaku Institute of Life Sciences 11 Minamiooya, Machida, Tokyo 194-8511, Japan

*To whom correspondence should be addressed. Tel: +81 3 3599 8051; Fax: +81 3 3599 8081; Email: m-suwa{at}aist.go.jp

Received February 14, 2005. Revised April 15, 2005. Accepted April 26, 2005.

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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