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Takahiko Muramatsu

Publications and source records attributed to Takahiko Muramatsu.

2 recordsLinked to original sources

Statistical analysis and prediction of functional residues effective for GPCR-G-protein coupling selectivity.

One of the important issues in G-protein-coupled receptor (GPCR) functional analysis is the mechanism of GPCR-G-protein coupling selectivity. G-proteins are classified into Gi/o, Gq/11 and Gs families. Although several experimental and computational analyses have been attempted, the mechanism remains unknown to this day. In this study, we have analyzed the multiple sequence alignments of GPCRs of known coupling selectivities by mapping onto the tertiary structure of rhodopsin. We identified several functional residue sites in GPCRs related to coupling selectivity, which are located mainly at the intracellular loops, and found that the occurrence of positively/negatively charged amino acids of the characteristic residues varies depending on the G-protein coupling selectivity. Especially, the occurrence of positively charged amino acids in receptors coupling to Gs family is less than that in receptors coupling to Gi/o and Gq/11 families. It is interesting that some characteristic residues are located near the extracellular terminus of transmembrane helices, which is far from the GPCR/G-protein binding interface. In most of the receptors coupling to Gs family, the occurrence of proline on the position corresponding to the 170th residue on rhodopsin is rare. These findings are vital to improving our understanding of the mechanism of G-protein coupling selectivity.

Amino Acid Sequence↗

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

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.

Artificial Intelligence↗