ArticleNucleic acids research2005
GRIFFIN: a system for predicting GPCR-G-protein coupling selectivity using a support vector machine and a hidden Markov model.
Article in Nucleic acids research, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Elucidating the Structure and Activation Mechanisms of GPCRs Using Modern Computational and AI Tools.Cell biochemistry and biophysics · 2026Review
- A Web Server for GPCR-GPCR Interaction Pair Prediction.Frontiers in endocrinology · 2022Article
- Understanding G Protein Selectivity of Muscarinic Acetylcholine Receptors Using Computational Methods.International journal of molecular sciences · 2019Article
- PRECOG: PREdicting COupling probabilities of G-protein coupled receptors.Nucleic acids research · 2019Article
- Article
- NRfamPred: a proteome-scale two level method for prediction of nuclear receptor proteins and their sub-families.Scientific reports · 2014Article
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- An approach for identifying cytokines based on a novel ensemble classifier.BioMed research international · 2013Article
- Prediction of functional class of proteins and peptides irrespective of sequence homology by support vector machines.Bioinformatics and biology insights · 2009Article
- GPCRTree: online hierarchical classification of GPCR function.BMC research notes · 2008Article
- Alternative splicing of the G protein-coupled receptor superfamily in human airway smooth muscle diversifies the complement of receptors.Proceedings of the National Academy of Sciences of the United States of America · 2008Article
- Advances in the Development and Application of Computational Methodologies for Structural Modeling of G-Protein Coupled Receptors.Expert opinion on drug discovery · 2008Article
- The satiety signaling neuropeptide perisulfakinin inhibits the activity of central neurons promoting general activity.Frontiers in cellular neuroscience · 2007Article
- Prediction of GPCR-G protein coupling specificity using features of sequences and biological functions.Genomics, proteomics & bioinformatics · 2006Article
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5 authors.
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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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