title: Membrane interactive alpha-helices in GPCRs as a novel drug target.
abstract: G-Protein Coupled Receptors (GPCRs) are one of the most important targets for pharmaceutical drug design. Over the past 30 years, mounting evidence has suggested the existence of homo and hetero dimers or higher-order complexes (oligomers) that are involved in signal transduction and some diseases. The number of reports describing GPCR oligomerization has increased, and in 2003, the organization of mouse rhodopsin into two-dimensional arrays of dimers was determined by an atomic force microscopic analysis. The analysis of the mouse rhodopsin complex has enabled us to discuss the oligomerization based on structural data. Although many unsolved problems still remains, the idea that GPCRs directly interact to form oligomers has been gradually accepted. One of the recent findings in the GPCR investigations is the clarification of the mechanisms of GPCR oligomerization at a molecular level. Most of these studies have suggested the importance of transmembrane alpha-helices for GPCR oligomerization. In this review, we will first summarize the importance of GPCR oligomerization and the functions of GPCRs. Then, we will explain the involvement of transmembrane alpha-helices in the oligomerization and a drug design strategy that targets these regions for GPCR oligomerization. Considering the current drug design methods, which are based on the modification of the protein-protein interactions of soluble regions of proteins, a "peptide mimic approach" that targets the transmembrane alpha-helices constituting the interfaces would be promising in drug discovery for GPCR oligomerization. For that purpose, we must know the positions of the interfaces. However, problems specific to membrane proteins have made it difficult to identify the positions of the interfaces experimentally. Therefore, information about the interfaces predicted by bioinformatics approaches is valuable. At the end of this review, several bioinformatics approaches toward interface prediction for oligomerization are introduced. The benefits and the pitfalls of these approaches are also discussed.
authors.affiliation: Graduate School of Information Science, Nara Institute of Science and Technology (NAIST), 8916-5 Ikoma, Nara 630-0101, Japan. wane@kuicr.kyoto-u.ac.jp
authors.forename: Wataru
authors.initials: W
authors.lastname: Nemoto
authors.forename: Hiroyuki
authors.initials: H
authors.lastname: Toh
mesh_terms: D001704:Biopolymers; D015195:Drug Design; D043562:Receptors, G-Protein-Coupled / Q000737:chemistry / Q000187:drug effects / Q000378:metabolism*
chemical_list: D001704:Biopolymers; D043562:Receptors, G-Protein-Coupled