Simulations of a G protein‐coupled receptor homology model predict dynamic features and a ligand binding site

Abstract

A computational approach to predict structures of rhodopsin-like G protein-coupled receptors (GPCRs) is presented and evaluated by comparison to the X-ray structural models. By combining sequence alignment, the rhodopsin crystal structure, and point mutation data on the b 2 adrenoreceptor (b2ar), we predict a (À)-epinephrine-bound computational model of the b 2 adrenoreceptor. The model is evaluated by molecular dynamics simulations and by comparison with the recent X-ray structures of b2ar. The overall correspondence between the predicted and the X-ray structural model is high. Especially the prediction of the ligand binding site is accurate. This shows that the proposed dynamic homology modelling approach can be used to create reasonable models for the understanding of structure and dynamics of other rhodopsin-like GPCRs.

How Does a Membrane Protein Achieve a Vectorial Proton Transfer Via Water Molecules?

“…Retinal parameters were taken from Kandt et al [14] The resulting model was placed into a pre-equillibrated POPC bilayer surrounded by a 1 m NaCl solution in accordance with Kandt et al [14] Simulations were carried out with GROMACS v3.3, merging the GROMOS96 force field and lipid parameters of Berger et al [28] in accordance with Schlegel et al [29] . The simulation parameters and protocol were based on Wolf et al [30] with a step size of 1 fs and no bond constraints. After protein/membrane equilibration, 20 ns of unrestrained MD simulation were performed at 293.15 K. The equilibration of the bR structure was judged by observation of the root mean square displacement of the heptahelical C a atoms (C a -RMSD, see Supporting Information).…”

Prediction of a Ligand‐Binding Niche within a Human Olfactory Receptor by Combining Site‐Directed Mutagenesis with Dynamic Homology Modeling

“…The performance of this approach was previously tested by homology modeling of the B2AR ligand-binding niche based on the rhodopsin template (see Supporting Information, Section 1 a). [40] Although the overall sequence identity among class A GPCRs is relatively low, this can be compensated for by careful incorporation of experimental information as constraints. [34] In the present study, site-directed mutagenesis and functional analysis of receptor mutants by Ca 2+ imaging were performed for validation of the hOR2AG1 homology model.…”

“…To better understand receptor activation, we thus searched for a dynamic ligand-protein interaction pattern instead of analyzing ligand-binding in static models. Therefore, in difference to other flexible GPCR ligand pocket analysis approaches, [34][35][36][37] we use the predictive power of protein/ligand complex molecular dynamics (MD) simulations [38][39][40] to gain insight into the protein-odorant dynamics necessary for receptor activation.We developed a dynamic model of the functionally wellcharacterized human olfactory receptor hOR2AG1.[41] We used an X-ray structure of bovine rhodopsin with 2.2 resolution [24] as starting structure for dynamic homology modeling of hOR2AG1, since both receptors belong to the class A GPCRs, and both harbor hydrophobic ligands. The performance of this approach was previously tested by homology modeling of the B2AR ligand-binding niche based on the rhodopsin template (see Supporting Information, Section 1 a).…”

Structural Model of Channelrhodopsin

“…In the first step, we build a homology model using the DChR sequence and the characteristic sequence pattern of helices A and B. Homology modeling has been successfully applied for structure prediction when the template sequence has high homology with that of the target protein, as recently shown for G protein-coupled receptors (12,13), which have seven transmembrane helices similar to the microbial rhodopsins. Based on the ASR structural template, we construct a three-dimensional structural model, which is subjected to extensive molecular dynamics (MD) simulations to examine its stability and dynamical features.…”