Construction of 3D models of the CYP11B family as a tool to predict ligand binding characteristics

Aldosterone is synthesised by aldosterone synthase (CYP11B2). CYP11B2 has a highly homologous isoform, steroid 11β-hydroxylase (CYP11B1), which is responsible for the biosynthesis of aldosterone precursors and glucocorticoids. To investigate aldosterone biosynthesis and facilitate the search for selective CYP11B2 inhibitors, we constructed three-dimensional models for CYP11B1 and CYP11B2 for both human and rat. The models were constructed based on the crystal structure of Pseudomonas Putida CYP101 and Oryctolagus Cuniculus CYP2C5. Small steric active site differences between the isoforms were found to be the most important determinants for the regioselective steroid synthesis. A possible explanation for these steric differences for the selective synthesis of aldosterone by CYP11B2 is presented. The activities of the known CYP11B inhibitors metyrapone, R-etomidate, R-fadrazole and S-fadrazole were determined using assays of V79MZ cells that express human CYP11B1 and CYP11B2, respectively. By investigating the inhibitors in the human CYP11B models using molecular docking and molecular dynamics simulations we were able to predict a similar trend in potency for the inhibitors as found in the in vitro assays. Importantly, based on the docking and dynamics simulations it is possible to understand the enantioselectivity of the human enzymes for the inhibitor fadrazole, the R-enantiomer being selective for CYP11B2 and the S-enantiomer being selective for CYP11B1

The James Webb Space Telescope Mission

Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least $4m$. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the $6.5m$ James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 years, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit.Comment: Accepted by PASP for the special issue on The James Webb Space Telescope Overview, 29 pages, 4 figure

Selection of compounds fitted into the pharmacophore.

<p><b>A)</b> Fitting of agonist kaempferol (gray) into the pharmacophore features <b>0</b>, <b>3</b>, <b>5</b>, <b>6</b>, <b>8</b>. Residues, which make hydrogen bonds to the agonist, are shown as sticks. <b>B)</b> Fitting of the blocker 4’-fluoro-6-methoxyflavanone (S-enantiomer, blue) into the pharmacophore features <b>0</b>, <b>1</b>, <b>2</b>, <b>7</b>, <b>8</b>. Residues, which make hydrogen bonds (yellow dashes) to the blocker, are shown as sticks. <b>C)</b> Fitting of kaempferol (gray), luteolin (pink), naringenin (green), and epicatechin (cyan). <b>D)</b> Fitting of the kaempferol (gray) and 4’-fluoro-6-methoxyflavanone (blue). The structures of 4’-fluoro-6-methoxyflavanone, luteolin, naringenin, and epicatechin are shown in <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0118200#pone.0118200.g004" target="_blank">Fig. 4</a></b>.</p

Best pharmacophore validation results per set and 5 or 6 feature combination.

<p>Best pharmacophore validation results per set and 5 or 6 feature combination.</p

Snooker pharmacophore features. Feature type, feature number, radius and residue contributions by amino acid number.

<p>Snooker pharmacophore features. Feature type, feature number, radius and residue contributions by amino acid number.</p

Pharmacophore results plot.

<p>5 feature pharmacophore lab set (black x), 5 feature pharmacophore literature set (green circle), 5 featured combined set (magenta plus), 6 feature pharmacophore lab set (red circle), 6 feature pharmacophore literature set (cyan box) and 6 featured combined set (orange diamond).</p

Homology model of the TM domains of hTAS2R39.

<p>TM I is depicted in dark blue, TM II in light blue, TM III in cyan, TM IV in light green, TM V in yellow, TM VI in orange, and TM VII in red. <b>A)</b> The Snooker pharmacophore hypothesis consists of acceptor features (numbers <b>0</b>, <b>1</b>, and <b>2</b> in gray), donor features (numbers <b>3</b>, <b>4</b>, and <b>5</b> in green), and hydrophobic features (numbers <b>6</b>, <b>7</b>, and <b>8</b> in magenta). Residues contributing to <b>B)</b> acceptor and donor features (<b>0</b>, <b>3</b>, and <b>5</b>) and <b>C)</b> hydrophobic features (<b>6</b>, and <b>8</b>) of the best performing feature combination are shown as sticks. All common rotamers are shown.</p

Comparative Analysis of Pharmacophore Screening Tools

The pharmacophore concept is of central importance in computer-aided drug design (CADD) mainly because of its successful application in medicinal chemistry and, in particular, high-throughput virtual screening (HTVS). The simplicity of the pharmacophore definition enables the complexity of molecular interactions between ligand and receptor to be reduced to a handful set of features. With many pharmacophore screening softwares available, it is of the utmost interest to explore the behavior of these tools when applied to different biological systems. In this work, we present a comparative analysis of eight pharmacophore screening algorithms (Catalyst, Unity, LigandScout, Phase, Pharao, MOE, Pharmer, and POT) for their use in typical HTVS campaigns against four different biological targets by using default settings. The results herein presented show how the performance of each pharmacophore screening tool might be specifically related to factors such as the characteristics of the binding pocket, the use of specific pharmacophore features, and the use of these techniques in specific steps/contexts of the drug discovery pipeline. Algorithms with rmsd-based scoring functions are able to predict more compound poses correctly as overlay-based scoring functions. However, the ratio of correctly predicted compound poses versus incorrectly predicted poses is better for overlay-based scoring functions that also ensure better performances in compound library enrichments. While the ensemble of these observations can be used to choose the most appropriate class of algorithm for specific virtual screening projects, we remarked that pharmacophore algorithms are often equally good, and in this respect, we also analyzed how pharmacophore algorithms can be combined together in order to increase the success of hit compound identification. This study provides a valuable benchmark set for further developments in the field of pharmacophore search algorithms, e.g., by using pose predictions and compound library enrichment criteria

Comparative analysis of pharmacophore screening tools.

Item does not contain fulltextThe pharmacophore concept is of central importance in computer-aided drug design (CADD) mainly because of its successful application in medicinal chemistry and, in particular, high-throughput virtual screening (HTVS). The simplicity of the pharmacophore definition enables the complexity of molecular interactions between ligand and receptor to be reduced to a handful set of features. With many pharmacophore screening softwares available, it is of the utmost interest to explore the behavior of these tools when applied to different biological systems. In this work, we present a comparative analysis of eight pharmacophore screening algorithms (Catalyst, Unity, LigandScout, Phase, Pharao, MOE, Pharmer, and POT) for their use in typical HTVS campaigns against four different biological targets by using default settings. The results herein presented show how the performance of each pharmacophore screening tool might be specifically related to factors such as the characteristics of the binding pocket, the use of specific pharmacophore features, and the use of these techniques in specific steps/contexts of the drug discovery pipeline. Algorithms with rmsd-based scoring functions are able to predict more compound poses correctly as overlay-based scoring functions. However, the ratio of correctly predicted compound poses versus incorrectly predicted poses is better for overlay-based scoring functions that also ensure better performances in compound library enrichments. While the ensemble of these observations can be used to choose the most appropriate class of algorithm for specific virtual screening projects, we remarked that pharmacophore algorithms are often equally good, and in this respect, we also analyzed how pharmacophore algorithms can be combined together in order to increase the success of hit compound identification. This study provides a valuable benchmark set for further developments in the field of pharmacophore search algorithms, e.g., by using pose predictions and compound library enrichment criteria