Influence of the HiPIMS voltage on the time resolved platinum ions energy distributions

International audienceHigh Power Impulse magnetron sputtering (HiPIMS) is a common way to create a high and dense ionized metallic vapor without the use of an alternative ionizing device, like radio frequency loops. HiPIMS has been used to perform the deposition of platinum thin films in order to control their morphology. This feature known to depend on the energy of the Pt species incoming onto the substrate during the deposition has to be carefully studied. Therefore, it's necessary to study the ions energy distribution during the sputtering pulse and to follow its evolution with the HiPIMS regime. Pictures of this evolution are presented

Improving virtual screening of G protein-coupled receptors via ligand-directed modeling

G protein-coupled receptors (GPCRs) play crucial roles in cell physiology and pathophysiology. There is increasing interest in using structural information for virtual screening (VS) of libraries and for structure-based drug design to identify novel agonist or antagonist leads. However, the sparse availability of experimentally determined GPCR/ligand complex structures with diverse ligands impedes the application of structure-based drug design (SBDD) programs directed to identifying new molecules with a select pharmacology. In this study, we apply ligand-directed modeling (LDM) to available GPCR X-ray structures to improve VS performance and selectivity towards molecules of specific pharmacological profile. The described method refines a GPCR binding pocket conformation using a single known ligand for that GPCR. The LDM method is a computationally efficient, iterative workflow consisting of protein sampling and ligand docking. We developed an extensive benchmark comparing LDM-refined binding pockets to GPCR X-ray crystal structures across seven different GPCRs bound to a range of ligands of different chemotypes and pharmacological profiles. LDM-refined models showed improvement in VS performance over origin X-ray crystal structures in 21 out of 24 cases. In all cases, the LDM-refined models had superior performance in enriching for the chemotype of the refinement ligand. This likely contributes to the LDM success in all cases of inhibitor-bound to agonist-bound binding pocket refinement, a key task for GPCR SBDD programs. Indeed, agonist ligands are required for a plethora of GPCRs for therapeutic intervention, however GPCR X-ray structures are mostly restricted to their inactive inhibitor-bound state

Expression and purification of recombinant G protein-coupled receptors: A review

Given their extensive role in cell signalling, GPCRs are significant drug targets; despite this, many of these receptors have limited or no available prophylaxis. Novel drug design and discovery significantly rely on structure determination, of which GPCRs are typically elusive. Progress has been made thus far to produce sufficient quantity and quality of protein for downstream analysis. As such, this review highlights the systems available for recombinant GPCR expression, with consideration of their advantages and disadvantages, as well as examples of receptors successfully expressed in these systems. Additionally, an overview is given on the use of detergents and the styrene maleic acid (SMA) co-polymer for membrane solubilisation, as well as purification techniques

Development of improved methods for predictive modelling of G protein-coupled receptors

G protein-coupled receptors (GPCRs) are a superfamily of transmembrane proteins that play crucial roles in cell physiology. Being key drug targets in many diseases, drug discovery efforts harness structural information about these receptors to rationally design drugs in the field of structure-based drug design (SBDD). This thesis presents the development of computational tools to manage and assess large GPCR structural model datasets, applies these tools to define key features linked with the best performing models in SBDD and integrates this knowledge into a novel computational workflow that predicts new GPCR models, which may offer new opportunities in SBDD programs

Structural features embedded in G protein-coupled receptor co-crystal structures are key to their success in virtual screening

<div><p>Structure based drug discovery on GPCRs harness atomic detail X-ray binding pockets and large libraries of potential drug lead candidates in virtual screening (VS) to identify novel lead candidates. Relatively small conformational differences between such binding pockets can be critical to the success of VS. Retrospective VS on GPCR/ligand co-crystal structures revealed stark differences in the ability of different structures to identify known ligands, despite being co-crystallized with the same ligand. When using the OpenEye toolkit and the ICM modeling package, we identify criteria associated with the predictive power of binding pockets in VS that consists of a combination of ligand/receptor interaction pattern and predicted ligand/receptor interaction strength. These findings can guide the selection and refinement of GPCR binding pockets for use in SBDD programs and may also provide a potential framework for evaluating the ability of computational GPCR binding pocket refinement tools in improving the predictive power of binding pockets.</p></div

Comparison of B2AR BI-bound binding pockets (3P0G, 3SN6 and 4LDE).

<p>(a) Binding pose overlay. (b, c) Binding pocket virtual screen results displayed as ROC curves of (b) B2AR agonists against decoys and (c) B2AR agonists against B2AR inhibitors. The ROC curves are representations of the VS, picking the best scoring ligand after docking three independent times. A black line depicts the hypothetical random recovery of true positives. The rank of the docked co-crystal ligand relative to the percentage false positives is identified with a vertical dashed line. All vertical lines are drawn but some may not be visible as they are hidden by the main curve and/or the y-axis. The inset values are the mean NSQ_AUC ± S.E.M. of three independent experiments. Statistical significance of binding pockets is reported in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0174719#pone.0174719.s027" target="_blank">S6 Table</a>, (d, e) Heavy atom RMSD comparison of X-ray structure with (d) bound ligands and (e) binding pocket residues. (f) Comparison of X-ray structures and docked poses: RMSD to X-ray ligand, ICM docking score of the docked ligand and ICM interactive scores.</p

Structural interaction fingerprints for the seven groups of co-crystal X-ray structures.

<p>AA2AR CGS-bound and ZM-bound, B1AR DOB-bound and CYP-bound, B2AR BI-bound and CAR-bound, DOR NAL-bound. Interactions were determined using toolbx_pdb between the bound ligand and its receptor. Interaction types include hydrophobic (blue), hydrogen bond donor and acceptor (red), weak hydrogen-bond donor and acceptor (orange), ionic (purple) and aromatic (green). White denotes the absence of interaction. Residues forming the binding pocket are annotated by residue type, residue number and location in the 7TM domain. For AA2AR, residue 89 was mutated to A in CGS-bound binding pockets and was wild type (Q) in ZM-bound binding pockets. Co-crystal structure of the same GPCR but different co-crystal ligand (agonist/inhibitor) are aligned to highlight the differences in interaction pattern.</p

Biased Agonism of Endogenous Opioid Peptides at the μ-Opioid Receptor

Copyright © 2015 by The American Society for Pharmacology and Experimental Therapeutics. Biased agonism is having a major impact on modern drug discovery, and describes the ability of distinct G protein-coupled receptor (GPCR) ligands to activate different cell signaling pathways, and to result in different physiologic outcomes. To date, most studies of biased agonism have focused on synthetic molecules targeting various GPCRs; however, many of these receptors have multiple endogenous ligands, suggesting that "natural" bias may be an unappreciated feature of these GPCRs. The μ-opioid receptor (MOP) is activated by numerous endogenous opioid peptides, remains an attractive therapeutic target for the treatment of pain, and exhibits biased agonism in response to synthetic opiates. The aim of this study was to rigorously assess the potential for biased agonism in the actions of endogenous opioids at the MOP in a common cellular background, and compare these to the effects of the agonist D-Ala2-N-MePhe4-Gly-ol enkephalin (DAMGO). We investigated activation of G proteins, inhibition of cAMP production, extracellular signal-regulated kinase 1 and 2 phosphorylation, β-arrestin 1/2 recruitment, and MOP trafficking, and applied a novel analytical method to quantify biased agonism. Although many endogenous opioids displayed signaling profiles similar to that of DAMGO, α-neoendorphin, Met-enkephalin-Arg-Phe, and the putatively endogenous peptide endomorphin-1 displayed particularly distinct bias profiles. These may represent examples of natural bias if it can be shown that they have different signaling properties and physiologic effects in vivo compared with other endogenous opioids. Understanding how endogenous opioids control physiologic processes through biased agonism can reveal vital information required to enable the design of biased opioids with improved pharmacological profiles and treat diseases involving dysfunction of the endogenous opioid system

GPCR X-ray crystal structures co-crystallized with a common orthosteric ligand.

<p>GPCR X-ray crystal structures co-crystallized with a common orthosteric ligand.</p

Comparison of B1AR CYP-bound binding pockets (2VT4, 2YCX, 2YCY and 4BVN).

<p>(a) Binding pose overlay. (b, c) Binding pocket virtual screen results displayed as ROC curves of (b) B1AR inhibitors against decoys and (c) B1AR inhibitors against B1AR agonists. The ROC curves are representations of the VS, picking the best scoring ligand after docking three independent times. A black line depicts the hypothetical random recovery of true positives. The rank of the docked co-crystal ligand relative to the percentage false positives is identified with a vertical dashed line. All vertical lines are drawn but some may not be visible as they are hidden by the main curve and/or the y-axis. The inset values are the mean NSQ_AUC ± S.E.M. of three independent experiments. Statistical significance of binding pockets is reported in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0174719#pone.0174719.s026" target="_blank">S5 Table</a>. (d, e) Heavy atom RMSD comparison of X-ray structure with (d) bound ligands and (e) binding pocket residues. (f) Comparison of X-ray structures and docked poses: RMSD to X-ray ligand, ICM docking score of the docked ligand and ICM interactive scores.</p