Gemini planet imager observational calibrations V: Astrometry and distortion

This is the final version of the article. Available from SPIE via the DOI in this record.From Conference Volume 9147: Ground-based and Airborne Instrumentation for Astronomy V, Suzanne K. Ramsay; Ian S. McLean; Hideki Takami, Montréal, Quebec, Canada, June 22, 2014We present the results of both laboratory and on sky astrometric characterization of the Gemini Planet Imager (GPI). This characterization includes measurement of the pixel scale∗ of the integral field spectrograph (IFS), the position of the detector with respect to north, and optical distortion. Two of these three quantities (pixel scale and distortion) were measured in the laboratory using two transparent grids of spots, one with a square pattern and the other with a random pattern. The pixel scale in the laboratory was also estimate using small movements of the artificial star unit (ASU) in the GPI adaptive optics system. On sky, the pixel scale and the north angle are determined using a number of known binary or multiple systems and Solar System objects, a subsample of which had concurrent measurements at Keck Observatory. Our current estimate of the GPI pixel scale is 14.14 ± 0.01 millarcseconds/pixel, and the north angle is -1.00 ± 0.03°. Distortion is shown to be small, with an average positional residual of 0.26 pixels over the field of view, and is corrected using a 5th order polynomial. We also present results from Monte Carlo simulations of the GPI Exoplanet Survey (GPIES) assuming GPI achieves ∼1 milliarcsecond relative astrometric precision. We find that with this precision, we will be able to constrain the eccentricities of all detected planets, and possibly determine the underlying eccentricity distribution of widely separated Jovians.The Gemini Observatory is operated by the Association of Universities for Research in Astronomy, Inc., under a cooperative agreement with the NSF on behalf of the Gemini partnership: the National Science Foundation (United States), the National Research Council (Canada), CONICYT (Chile), the Australian Research Council (Australia), Ministério da Ciência, Tecnologia e Inovação (Brazil), and Ministerio de Ciencia, Tecnología e Innovación Productiva (Argentina). This publication makes use of data obtained at the W.M. Keck Observatory, which is operated as a scientific partnership among the California Institute of Technology, the University of California and the National Aeronautics and Space Administration. The Observatory was made possible by the generous financial support of the W.M. Keck Foundation. P.K. and J.R.G. thank support from NASA NNX11AD21G, NSF AST-0909188, and the University of California LFRP-118057. Q.M.K is a Dunlap Fellow at the Dunlap Institute for Astronomy & Astrophysics, University of Toronto. The Dunlap Institute is funded through an endowment established by the David Dunlap family and the University of Toronto

Novel sialic acid derivatives lock open the 150-loop of an influenza A virus group-1 sialidase

Influenza virus sialidase has an essential role in the virus' life cycle. Two distinct groups of influenza A virus sialidases have been established, that differ in the flexibility of the '150-loop', providing a more open active site in the apo form of the group-1 compared to group-2 enzymes. In this study we show, through a multidisciplinary approach, that novel sialic acid-based derivatives can exploit this structural difference and selectively inhibit the activity of group-1 sialidases. We also demonstrate that group-1 sialidases from drug-resistant mutant influenza viruses are sensitive to these designed compounds. Moreover, we have determined, by protein X-ray crystallography, that these inhibitors lock open the group-1 sialidase flexible 150-loop, in agreement with our molecular modelling prediction. This is the first direct proof that compounds may be developed to selectively target the pandemic A/H1N1, avian A/H5N1 and other group-1 sialidase-containing viruses, based on an open 150-loop conformation of the enzyme

Cholera Toxin B Subunits Assemble into Pentamers - Proposition of a Fly-Casting Mechanism

The cholera toxin B pentamer (CtxB5), which belongs to the AB5 toxin family, is used as a model study for protein assembly. The effect of the pH on the reassembly of the toxin was investigated using immunochemical, electrophoretic and spectroscopic methods. Three pH-dependent steps were identified during the toxin reassembly: (i) acquisition of a fully assembly-competent fold by the CtxB monomer, (ii) association of CtxB monomer into oligomers, (iii) acquisition of the native fold by the CtxB pentamer. The results show that CtxB5 and the related heat labile enterotoxin LTB5 have distinct mechanisms of assembly despite sharing high sequence identity (84%) and almost identical atomic structures. The difference can be pinpointed to four histidines which are spread along the protein sequence and may act together. Thus, most of the toxin B amino acids appear negligible for the assembly, raising the possibility that assembly is driven by a small network of amino acids instead of involving all of them

Adhesion Molecules Associated with Female Genital Tract Infection

Altres ajuts: Marie Curie Career Integration Grant i una beca Fundació Dexeus Salut de la DonaEfforts to develop vaccines that can elicit mucosal immune responses in the female genital tract against sexually transmitted infections have been hampered by an inability to measure immune responses in these tissues. The differential expression of adhesion molecules is known to confer site-dependent homing of circulating effector T cells to mucosal tissues. Specific homing molecules have been defined that can be measured in blood as surrogate markers of local immunity (e.g. α4β7 for gut). Here we analyzed the expression pattern of adhesion molecules by circulating effector T cells following mucosal infection of the female genital tract in mice and during a symptomatic episode of vaginosis in women. While CCR2, CCR5, CXCR6 and CD11c were preferentially expressed in a mouse model of Chlamydia infection, only CCR5 and CD11c were clearly expressed by effector T cells during bacterial vaginosis in women. Other homing molecules previously suggested as required for homing to the genital mucosa such as α4β1 and α4β7 were also differentially expressed in these patients. However, CD11c expression, an integrin chain rarely analyzed in the context of T cell immunity, was the most consistently elevated in all activated effector CD8+ T cell subsets analyzed. This molecule was also induced after systemic infection in mice, suggesting that CD11c is not exclusive of genital tract infection. Still, its increase in response to genital tract disorders may represent a novel surrogate marker of mucosal immunity in women, and warrants further exploration for diagnostic and therapeutic purposes

Machine learning for regulatory analysis and transcription factor target prediction in yeast

High throughput technologies, including array-based chromatin immunoprecipitation, have rapidly increased our knowledge of transcriptional maps—the identity and location of regulatory binding sites within genomes. Still, the full identification of sites, even in lower eukaryotes, remains largely incomplete. In this paper we develop a supervised learning approach to site identification using support vector machines (SVMs) to combine 26 different data types. A comparison with the standard approach to site identification using position specific scoring matrices (PSSMs) for a set of 104 Saccharomyces cerevisiae regulators indicates that our SVM-based target classification is more sensitive (73 vs. 20%) when specificity and positive predictive value are the same. We have applied our SVM classifier for each transcriptional regulator to all promoters in the yeast genome to obtain thousands of new targets, which are currently being analyzed and refined to limit the risk of classifier over-fitting. For the purpose of illustration we discuss several results, including biochemical pathway predictions for Gcn4 and Rap1. For both transcription factors SVM predictions match well with the known biology of control mechanisms, and possible new roles for these factors are suggested, such as a function for Rap1 in regulating fermentative growth. We also examine the promoter melting temperature curves for the targets of YJR060W, and show that targets of this TF have potentially unique physical properties which distinguish them from other genes. The SVM output automatically provides the means to rank dataset features to identify important biological elements. We use this property to rank classifying k-mers, thereby reconstructing known binding sites for several TFs, and to rank expression experiments, determining the conditions under which Fhl1, the factor responsible for expression of ribosomal protein genes, is active. We can see that targets of Fhl1 are differentially expressed in the chosen conditions as compared to the expression of average and negative set genes. SVM-based classifiers provide a robust framework for analysis of regulatory networks. Processing of classifier outputs can provide high quality predictions and biological insight into functions of particular transcription factors. Future work on this method will focus on increasing the accuracy and quality of predictions using feature reduction and clustering strategies. Since predictions have been made on only 104 TFs in yeast, new classifiers will be built for the remaining 100 factors which have available binding data

Calcium Triggered Lα-H2 Phase Transition Monitored by Combined Rapid Mixing and Time-Resolved Synchrotron SAXS

BACKGROUND: Awad et al. reported on the Ca(2+)-induced transitions of dioleoyl-phosphatidylglycerol (DOPG)/monoolein (MO) vesicles to bicontinuous cubic phases at equilibrium conditions. In the present study, the combination of rapid mixing and time-resolved synchrotron small-angle X-ray scattering (SAXS) was applied for the in-situ investigations of fast structural transitions of diluted DOPG/MO vesicles into well-ordered nanostructures by the addition of low concentrated Ca(2+) solutions. METHODOLOGY/PRINCIPAL FINDINGS: Under static conditions and the in absence of the divalent cations, the DOPG/MO system forms large vesicles composed of weakly correlated bilayers with a d-spacing of approximately 140 A (L(alpha)-phase). The utilization of a stopped-flow apparatus allowed mixing these DOPG/MO vesicles with a solution of Ca(2+) ions within 10 milliseconds (ms). In such a way the dynamics of negatively charged PG to divalent cation interactions, and the kinetics of the induced structural transitions were studied. Ca(2+) ions have a very strong impact on the lipidic nanostructures. Intriguingly, already at low salt concentrations (DOPG/Ca(2+)>2), Ca(2+) ions trigger the transformation from bilayers to monolayer nanotubes (inverted hexagonal phase, H(2)). Our results reveal that a binding ratio of 1 Ca(2+) per 8 DOPG is sufficient for the formation of the H(2) phase. At 50 degrees C a direct transition from the vesicles to the H(2) phase was observed, whereas at ambient temperature (20 degrees C) a short lived intermediate phase (possibly the cubic Pn3m phase) coexisting with the H(2) phase was detected. CONCLUSIONS/SIGNIFICANCE: The strong binding of the divalent cations to the negatively charged DOPG molecules enhances the negative spontaneous curvature of the monolayers and causes a rapid collapsing of the vesicles. The rapid loss of the bilayer stability and the reorganization of the lipid molecules within ms support the argument that the transition mechanism is based on a leaky fusion of the vesicles

Computational design of self-assembling cyclic protein homo-oligomers

Self-assembling cyclic protein homo-oligomers play important roles in biology, and the ability to generate custom homo-oligomeric structures could enable new approaches to probe biological function. Here we report a general approach to design cyclic homo-oligomers that employs a new residue-pair-transform method to assess the designability of a protein-protein interface. This method is sufficiently rapid to enable the systematic enumeration of cyclically docked arrangements of a monomer followed by sequence design of the newly formed interfaces. We use this method to design interfaces onto idealized repeat proteins that direct their assembly into complexes that possess cyclic symmetry. Of 96 designs that were characterized experimentally, 21 were found to form stable monodisperse homo-oligomers in solution, and 15 (four homodimers, six homotrimers, six homotetramers and one homopentamer) had solution small-angle X-ray scattering data consistent with the design models. X-ray crystal structures were obtained for five of the designs and each is very close to their corresponding computational model

Predicted binding site information improves model ranking in protein docking using experimental and computer-generated target structures

BACKGROUND: Protein-protein interactions (PPIs) mediate the vast majority of biological processes, therefore, significant efforts have been directed to investigate PPIs to fully comprehend cellular functions. Predicting complex structures is critical to reveal molecular mechanisms by which proteins operate. Despite recent advances in the development of new methods to model macromolecular assemblies, most current methodologies are designed to work with experimentally determined protein structures. However, because only computer-generated models are available for a large number of proteins in a given genome, computational tools should tolerate structural inaccuracies in order to perform the genome-wide modeling of PPIs. RESULTS: To address this problem, we developed eRank(PPI), an algorithm for the identification of near-native conformations generated by protein docking using experimental structures as well as protein models. The scoring function implemented in eRank(PPI) employs multiple features including interface probability estimates calculated by eFindSite(PPI) and a novel contact-based symmetry score. In comparative benchmarks using representative datasets of homo- and hetero-complexes, we show that eRank(PPI) consistently outperforms state-of-the-art algorithms improving the success rate by ~10 %. CONCLUSIONS: eRank(PPI) was designed to bridge the gap between the volume of sequence data, the evidence of binary interactions, and the atomic details of pharmacologically relevant protein complexes. Tolerating structure imperfections in computer-generated models opens up a possibility to conduct the exhaustive structure-based reconstruction of PPI networks across proteomes. The methods and datasets used in this study are available at www.brylinski.org/erankppi