title: SuperBiHelix method for predicting the pleiotropic ensemble of G-protein-coupled receptor conformations.
abstract: There is overwhelming evidence that G-protein-coupled receptors (GPCRs) exhibit several distinct low-energy conformations, each of which might favor binding to different ligands and/or lead to different downstream functions. Understanding the function of such proteins requires knowledge of the ensemble of low-energy configurations that might play a role in this pleiotropic functionality. We earlier reported the BiHelix method for efficiently sampling the (12)(7) = 35 million conformations resulting from 30° rotations about the axis (η) of all seven transmembrane helices (TMHs), showing that the experimental structure is reliably selected as the best conformation from this ensemble. However, various GPCRs differ sufficiently in the tilts of the TMHs that this method need not predict the optimum conformation starting from any other template. In this paper, we introduce the SuperBiHelix method in which the tilt angles (θ, ϕ) are optimized simultaneously with rotations (η) efficiently enough that it is practical and sufficient to sample (5 × 3 × 5)(7) = 13 trillion configurations. This method can correctly identify the optimum structure of a GPCR starting with the template from a different GPCR. We have validated this method by predicting known crystal structure conformations starting from the template of a different protein structure. We find that the SuperBiHelix conformational ensemble includes the higher energy conformations associated with the active protein in addition to those associated with the more stable inactive protein. This methodology was then applied to design and experimentally confirm structures of three mutants of the CB1 cannabinoid receptor associated with different functions.
authors.affiliation: Materials and Process Simulation Center, California Institute of Technology, Pasadena, CA 91125.
authors.forename: Jenelle K
authors.initials: JK
authors.lastname: Bray
authors.forename: Ravinder
authors.initials: R
authors.lastname: Abrol
authors.forename: William A
authors.initials: WA
authors.lastname: Goddard
authors.forename: Bartosz
authors.initials: B
authors.lastname: Trzaskowski
authors.forename: Caitlin E
authors.initials: CE
authors.lastname: Scott
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D019295:Computational Biology; D018360:Crystallography, X-Ray; D006801:Humans; D008024:Ligands; D062105:Molecular Docking Simulation / Q000379:methods*; D009154:Mutation; D011485:Protein Binding; D017434:Protein Structure, Tertiary; D043705:Receptor, Adenosine A2A / Q000737:chemistry; D043884:Receptor, Cannabinoid, CB1 / Q000737:chemistry; D018343:Receptors, Adrenergic, beta-2 / Q000737:chemistry; D043562:Receptors, G-Protein-Coupled / Q000737:chemistry*; D012984:Software
chemical_list: D008024:Ligands; D043705:Receptor, Adenosine A2A; D043884:Receptor, Cannabinoid, CB1; D018343:Receptors, Adrenergic, beta-2; D043562:Receptors, G-Protein-Coupled
keywords: A2A adenosine receptor; constitutive activity; functional selectivity; protein structure prediction; β2-adrenergic receptor