Prediction of a ligand-binding niche within a human olfactory receptor by combining site-directed mutagenesis with dynamic homology modeling.

The mammalian olfactory system comprises a large family of G protein-coupled receptors (GPCRs) to detect and discriminate numerous volatile ligands. More than 350 human genes encode functional olfactory receptors (ORs) that belong to the class A (rhodopsin-like) GPCR family. Owing to difficulties with functional OR expression in heterologous systems, only a few humanORs have been characterized to date. Most deorphanized ORs, that is, ORs with a known ligand spectrum, detect multiple chemically similar odorants, and hypervariable residues in the seven transmembrane (7TM) helices (I–VII) have been postulated to form the basis for ligand specificity. A prerequisite for understanding olfactory receptor selectivity is information on the spatial properties of the ligand-binding niche. Different classes of approaches have been employed for such an assessment: Ligand-based approaches, such as pharmacophore modeling or quantitative structure-activity relationship (QSAR), can give valuable models of the ligand structure, which is required for discriminating activating and inactive ligands, and information on the form of the binding pocket. Receptor-based approaches such as homology modeling create a model of the protein and the binding site explicitly, and from this give information on ligand binding. Both techniques can be combined together. For such receptor-based and mixed approaches, the X-ray structures of seven GPCRs have been solved to date, but none for ORs. Previous studies have used static structural models of different ORs based on a rhodopsin and a b2-adrenergic receptor (B2AR) [33] template. However, most odorants are highly flexible, so assessment of the ligand/protein dynamics might be of crucial importance in understanding ligand recognition by ORs. 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, we use the predictive power of protein/ligand complex molecular dynamics (MD) simulations 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. We used an X-ray structure of bovine rhodopsin with 2.2 resolution 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 1a). 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. In the present study, site-directed mutagenesis and functional analysis of receptor mutants by Ca imaging were performed for validation of the hOR2AG1 homology model. Combining both techniques in a cycle of dynamic computational predictions and experimental analysis based on site-directed mutagenesis, we were able to characterize and refine the three-dimensional structure of the [*] Prof. Dr. K. Gerwert Lehrstuhl f r Biophysik, Ruhr-University Bochum Universit tsstrasse 150, 44780 Bochum (Germany) E-mail: [email protected] Homepage: http://www.bph.ruhr-uni-bochum.de/ Dr. S. Wolf, Prof. Dr. K. Gerwert Department of Biophysics, CAS-Max-Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences 320 Yue Yang Road, 200031 Shanghai (P.R. China) Dr. L. Gelis, Prof. Dr. H. Hatt Lehrstuhl f r Zellphysiologie Ruhr-University Bochum (Germany) Prof. Dr. E. M. Neuhaus Neuroscience Research Center, Cluster of Excellence NeuroCure Charit -Universit tsmedizin Berlin Charit platz 1, 10117 Berlin (Germany) [] L.G. and S.W. contributed equally to the technical research, E.M.N. and K.G. contributed equally to the supervision of the study.