S were prepared from vehicle- or DMBA-treated females (9?0 weeks of age) and fixed for analysis. Representative flow cytograms are shown, gated to eliminate debris (a,e; side scatter, SSC-A, versus forward scatter, FSC-A), to eliminate cell doublets and aggregates (b,f; DAPI area versus DAPI width gate), to eliminate nonepithelial cells (c,g; APC-CD45/CD31 versus forward scatter,Genotoxins Inhibit Wnt-Dependent Mammary Stem Cell(quantitation of these cultures from n = 2; cell number scored = 500). (TIF)and the members of the Flow Cytometry core facility supported by the University of Wisconsin Carbone Cancer Center.Author Contributions AcknowledgmentsWe thank Lance Rodenkirch from the W.M. Keck Laboratory for Biological Imaging at the University of Wisconsin for his expert assistance, Conceived and designed the experiments: KSK SK CMA. Performed the experiments: KSK SK. Analyzed the data: KSK SK CMA. Wrote the paper: KSK CMA.
G protein-coupled receptors (GPCRs) are one of the pharmaceutically most important protein families, and the targets of around one third of ENMD-2076 site present day drugs [1]. They mediate the transmission of signals from the exterior to the interior of a cell by binding signaling agents and, via conformational changes, eliciting intracellular responses. GPCRs consist of seven membranecrossing helices. The binding pockets of the native small molecule ligands, i.e. orthosteric binding sites, are situated in the middle of the helical bundle in the Class A GPCR structures that have been determined so far [2]. Despite the recent advances in GPCR X-ray structure determination [3] and the substantial numbers 1480666 of novel ligands identified for some GPCRs [4,5], there are still many (potential) GPCR targets for which no structure or ligands are known. In order to apply protein structure-based methods of ligand identification, in particular docking, to receptors that lack an experimentally determined structure, homology modeling is a promising avenue. Constructing homology models is facilitated by the fact that the transmembrane (TM) region of Class A GPCRs is relatively well conserved [6]. The accuracy of homology models is limited, however, by the uncertainty of modeling the extra- and intracellular loops, which greatly vary in length and amino acid composition, even between otherwise closely related GPCRs [7]. In this study, we tested the utility of homology models for docking and selecting compounds with reasonable affinity for theinvestigated receptor subtype. We intentionally restricted the amount of existing ligand data used to refine the binding site during model building to mimic a situation where few ligands are known (as would be the case for previously little investigated “novel” targets). In fact, except for 15857111 the very first steps of model building and optimization, only the affinity data obtained in this study was used to improve the homology models. Three sequential cycles of model refinement, docking, and ligand testing were applied, using the data acquired in B1939 mesylate previous rounds to guide the receptor model optimization in the following rounds. In parallel, we also probed the tendency of the screen to identify novel ligands of other subtypes within the same receptor family, i.e. the selectivity of a homology model-based screen against a single GPCR subtype. These findings were compared with the distribution of selectivity ratios of known ligands for the same subtypes. The adenosine receptors (ARs), which consist of the four subt.S were prepared from vehicle- or DMBA-treated females (9?0 weeks of age) and fixed for analysis. Representative flow cytograms are shown, gated to eliminate debris (a,e; side scatter, SSC-A, versus forward scatter, FSC-A), to eliminate cell doublets and aggregates (b,f; DAPI area versus DAPI width gate), to eliminate nonepithelial cells (c,g; APC-CD45/CD31 versus forward scatter,Genotoxins Inhibit Wnt-Dependent Mammary Stem Cell(quantitation of these cultures from n = 2; cell number scored = 500). (TIF)and the members of the Flow Cytometry core facility supported by the University of Wisconsin Carbone Cancer Center.Author Contributions AcknowledgmentsWe thank Lance Rodenkirch from the W.M. Keck Laboratory for Biological Imaging at the University of Wisconsin for his expert assistance, Conceived and designed the experiments: KSK SK CMA. Performed the experiments: KSK SK. Analyzed the data: KSK SK CMA. Wrote the paper: KSK CMA.
G protein-coupled receptors (GPCRs) are one of the pharmaceutically most important protein families, and the targets of around one third of present day drugs [1]. They mediate the transmission of signals from the exterior to the interior of a cell by binding signaling agents and, via conformational changes, eliciting intracellular responses. GPCRs consist of seven membranecrossing helices. The binding pockets of the native small molecule ligands, i.e. orthosteric binding sites, are situated in the middle of the helical bundle in the Class A GPCR structures that have been determined so far [2]. Despite the recent advances in GPCR X-ray structure determination [3] and the substantial numbers 1480666 of novel ligands identified for some GPCRs [4,5], there are still many (potential) GPCR targets for which no structure or ligands are known. In order to apply protein structure-based methods of ligand identification, in particular docking, to receptors that lack an experimentally determined structure, homology modeling is a promising avenue. Constructing homology models is facilitated by the fact that the transmembrane (TM) region of Class A GPCRs is relatively well conserved [6]. The accuracy of homology models is limited, however, by the uncertainty of modeling the extra- and intracellular loops, which greatly vary in length and amino acid composition, even between otherwise closely related GPCRs [7]. In this study, we tested the utility of homology models for docking and selecting compounds with reasonable affinity for theinvestigated receptor subtype. We intentionally restricted the amount of existing ligand data used to refine the binding site during model building to mimic a situation where few ligands are known (as would be the case for previously little investigated “novel” targets). In fact, except for 15857111 the very first steps of model building and optimization, only the affinity data obtained in this study was used to improve the homology models. Three sequential cycles of model refinement, docking, and ligand testing were applied, using the data acquired in previous rounds to guide the receptor model optimization in the following rounds. In parallel, we also probed the tendency of the screen to identify novel ligands of other subtypes within the same receptor family, i.e. the selectivity of a homology model-based screen against a single GPCR subtype. These findings were compared with the distribution of selectivity ratios of known ligands for the same subtypes. The adenosine receptors (ARs), which consist of the four subt.
