出版社：The Japanese Society for Artificial Intelligence
摘要：G-Protein coupled receptors (GPCRs) comprise a large superfamily of proteins and are a target for nearly 50% of drugs in clinical use today. GPCRs have a unique structural motif, seven transmembrane helices, and it is known that agonists and antagonists dock with a GPCR in its ``active'' and ``inactive'' condition, respectively. Knowing conformations of both states is eagerly anticipated for elucidation of drug action mechanism. Since GPCRs are difficult to crystallize, the 3D structures of these receptors have not yet been determined by X-ray crystallography, except the inactive-state conformation of two proteins. The conformation of them enabled the inactive form of other GPCRs to be modeled by computer-aided homology modeling. However, to date, the active form of GPCRs has not been solved. This paper describes a novel method to predict the 3D structure of an active-state GPCR aiming at molecular docking-based virtual screening using real-coded genetic algorithm (real-coded GA), receptor-ligand docking simulations, and molecular dynamics (MD) simulations. The basic idea of the method is that the MD is first used to calculate an average 3D coordinates of all atoms of a GPCR protein against heat fluctuation on the pico- or nano- second time scale, and then real-coded GA involving receptor-ligand docking simulations functions to determine the rotation angle of each helix as a movement on wider time scale. The method was validated using human leukotriene B4 receptor BLT1 as a sample GPCR. Our study demonstrated that the established evolutionary search for the active state of the leukotriene receptor provided the appropriate 3D structure of the receptor to dock with its agonists.
