Tropospheric distribution of sulphate aerosol mass and number concentration during INDOEX-IFP and its transport over the Indian Ocean: a GCM study

International audienceAn interactive sulphate aerosol chemistry module has been incorporated in the Laboratoire de Météorologie Dynamique General Circulation Model (LMD-GCM) to simulate the sulphur chemistry during the Indian Ocean Experiment (INDOEX) Intensive Field Phase-1999 (INDOEX-IFP). The originality of this module is its ability to predict particle mass and number concentration for the Aitken and accumulation modes. The model qualitatively reproduces the spatial patterns of observations on sulphate aerosol during INDOEX. On the basis of size distribution retrieved from the observations made along the cruise route during 1998 and 1999, the model successfully simulates the order of magnitude and the general north-south gradient in aerosol number concentration. The result shows the southward migration of minimum concentrations, which follows ITCZ (Inter Tropical Convergence Zone) migration. Sulphate surface concentration during INDOEX-IFP at Kaashidhoo (73.46° E, 4.96° N) gives an agreement within a factor of 2 to 3. Predicted sulphate aerosol optical depth (AOD) matches reasonably with measured values, indicating the capability of this model to predict the vertically integrated column sulphate burden. The Indian contribution to estimated sulphate burden over India is more than 60% with values upto 40% over the Arabian Sea

Dominant aerosol processes during high-pollution episodes over Greater Tokyo

This paper studies two high-pollution episodes over Greater Tokyo: 9 and 10 December 1999, and 31 July and 1 August 2001. Results obtained with the chemistry-transport model (CTM) Polair3D are compared to measurements of inorganic PM2.5. To understand to which extent the aerosol processes modeled in Polair3D impact simulated inorganic PM2.5, Polair3D is run with different options in the aerosol module, e.g. with/without heterogeneous reactions. To quantify the impact of processes outside the aerosol module, simulations are also done with another CTM (CMAQ). In the winter episode, sulfate is mostly impacted by condensation, coagulation, long-range transport, and deposition to a lesser extent. In the summer episode, the effect of long-range transport largely dominates. The impact of condensation/evaporation is dominant for ammonium, nitrate and chloride in both episodes. However, the impact of the thermodynamic equilibrium assumption is limited. The impact of heterogeneous reactions is large for nitrate and ammonium, and taking heterogeneous reactions into account appears to be crucial in predicting the peaks of nitrate and ammonium. The impact of deposition is the same for all inorganic PM2.5. It is small compared to the impact of other processes although it is not negligible. The impact of nucleation is negligible in the summer episode, and small in the winter episode. The impact of coagulation is larger in the winter episode than in the summer episode, because the number of small particles is higher in the winter episode as a consequence of nucleation.Comment: Journal of Geophysical Research D: Atmospheres (15/05/2007) in pres

Developmental Methylmercury Exposure Affects Swimming Behavior and Foraging Efficiency of Yellow Perch (Perca flavescens) Larvae

Methylmercury (MeHg) is a pervasive and ubiquitous environmental neurotoxicant within aquatic ecosystems, known to alter behavior in fish and other vertebrates. This study sought to assess the behavioral effects of developmental MeHg exposure on larval yellow perch (Perca flavescens)--a nonmodel fish species native to the Great Lakes. Embryos were exposed to MeHg (0, 30, 100, 300, and 1000 nM) for 20 h and then reared to 25 days post fertilization (dpf) for analyses of spontaneous swimming, visual motor response (VMR), and foraging efficiency. MeHg exposures rendered total mercury (THg) body burdens of 0.02, 0.21, 0.95, 3.14, and 14.93 μg/g (wet weight). Organisms exposed to 1000 nM exhibited high mortality; thus, they were excluded from downstream behavioral analyses. All MeHg exposures tested were associated with a reduction in spontaneous swimming at 17 and 25 dpf. Exposure to 30 and 100 nM MeHg caused altered locomotor output during the VMR assay at 21 dpf, whereas exposure to 100 nM MeHg was associated with decreased foraging efficiency at 25 dpf. For the sake of comparison, the secondlowest exposure tested here rendered a THg burden that represents the permissible level of consumable fish in the United States. Moreover, this dose is reported in roughly two-thirds of consumable fish species monitored in the United States, according to the Food and Drug Administration. Although the THg body burdens reported here were higher than expected in the environment, our study is the first to analyze the effects of MeHg exposure on fundamental survival behaviors of yellow perch larvae and advances in the exploration of the ecological relevance of behavioral end points

Voronoia: analyzing packing in protein structures

The packing of protein atoms is an indicator for their stability and functionality, and applied in determining thermostability, in protein design, ligand binding and to identify flexible regions in proteins. Here, we present Voronoia, a database of atomic-scale packing data for protein 3D structures. It is based on an improved Voronoi Cell algorithm using hyperboloid interfaces to construct atomic volumes, and to resolve solvent-accessible and -inaccessible regions of atoms. The database contains atomic volumes, local packing densities and interior cavities calculated for 61 318 biological units from the PDB. A report for each structure summarizes the packing by residue and atom types, and lists the environment of interior cavities. The packing data are compared to a nonredundant set of structures from SCOP superfamilies. Both packing densities and cavities can be visualized in the 3D structures by the Jmol plugin. Additionally, PDB files can be submitted to the Voronoia server for calculation. This service performs calculations for most full-atomic protein structures within a few minutes. For batch jobs, a standalone version of the program with an optional PyMOL plugin is available for download. The database can be freely accessed at: http://bioinformatics.charite.de/voronoia

FLORA: a novel method to predict protein function from structure in diverse superfamilies

Predicting protein function from structure remains an active area of interest, particularly for the structural genomics initiatives where a substantial number of structures are initially solved with little or no functional characterisation. Although global structure comparison methods can be used to transfer functional annotations, the relationship between fold and function is complex, particularly in functionally diverse superfamilies that have evolved through different secondary structure embellishments to a common structural core. The majority of prediction algorithms employ local templates built on known or predicted functional residues. Here, we present a novel method (FLORA) that automatically generates structural motifs associated with different functional sub-families (FSGs) within functionally diverse domain superfamilies. Templates are created purely on the basis of their specificity for a given FSG, and the method makes no prior prediction of functional sites, nor assumes specific physico-chemical properties of residues. FLORA is able to accurately discriminate between homologous domains with different functions and substantially outperforms (a 2–3 fold increase in coverage at low error rates) popular structure comparison methods and a leading function prediction method. We benchmark FLORA on a large data set of enzyme superfamilies from all three major protein classes (α, β, αβ) and demonstrate the functional relevance of the motifs it identifies. We also provide novel predictions of enzymatic activity for a large number of structures solved by the Protein Structure Initiative. Overall, we show that FLORA is able to effectively detect functionally similar protein domain structures by purely using patterns of structural conservation of all residues

High throughput mutagenesis for identification of residues regulating human prostacyclin (hIP) receptor

The human prostacyclin receptor (hIP receptor) is a seven-transmembrane G protein-coupled receptor (GPCR) that plays a critical role in vascular smooth muscle relaxation and platelet aggregation. hIP receptor dysfunction has been implicated in numerous cardiovascular abnormalities, including myocardial infarction, hypertension, thrombosis and atherosclerosis. Genomic sequencing has discovered several genetic variations in the PTGIR gene coding for hIP receptor, however, its structure-function relationship has not been sufficiently explored. Here we set out to investigate the applicability of high throughput random mutagenesis to study the structure-function relationship of hIP receptor. While chemical mutagenesis was not suitable to generate a mutagenesis library with sufficient coverage, our data demonstrate error-prone PCR (epPCR) mediated mutagenesis as a valuable method for the unbiased screening of residues regulating hIP receptor function and expression. Here we describe the generation and functional characterization of an epPCR derived mutagenesis library compromising >4000 mutants of the hIP receptor. We introduce next generation sequencing as a useful tool to validate the quality of mutagenesis libraries by providing information about the coverage, mutation rate and mutational bias. We identified 18 mutants of the hIP receptor that were expressed at the cell surface, but demonstrated impaired receptor function. A total of 38 non-synonymous mutations were identified within the coding region of the hIP receptor, mapping to 36 distinct residues, including several mutations previously reported to affect the signaling of the hIP receptor. Thus, our data demonstrates epPCR mediated random mutagenesis as a valuable and practical method to study the structurefunction relationship of GPCRs. © 2014 Bill et al

A 41,500 year-old decorated ivory pendant from Stajnia Cave (Poland)

Evidence of mobiliary art and body augmentation are associated with the cultural innovations introduced by Homo sapiens at the beginning of the Upper Paleolithic. Here, we report the discovery of the oldest known human-modified punctate ornament, a decorated ivory pendant from the Paleolithic layers at Stajnia Cave in Poland. We describe the features of this unique piece, as well as the stratigraphic context and the details of its chronometric dating. The Stajnia Cave plate is a personal 'jewellery' object that was created 41,500 calendar years ago (directly radiocarbon dated). It is the oldest known of its kind in Eurasia and it establishes a new starting date for a tradition directly connected to the spread of modern Homo sapiens in Europe

Ligand binding site superposition and comparison based on Atomic Property Fields: identification of distant homologues, convergent evolution and PDB-wide clustering of binding sites

A new binding site comparison algorithm using optimal superposition of the continuous pharmacophoric property distributions is reported. The method demonstrates high sensitivity in discovering both, distantly homologous and convergent binding sites. Good quality of superposition is also observed on multiple examples. Using the new approach, a measure of site similarity is derived and applied to clustering of ligand binding pockets in PDB

Prediction of catalytic residues using Support Vector Machine with selected protein sequence and structural properties

BACKGROUND: The number of protein sequences deriving from genome sequencing projects is outpacing our knowledge about the function of these proteins. With the gap between experimentally characterized and uncharacterized proteins continuing to widen, it is necessary to develop new computational methods and tools for functional prediction. Knowledge of catalytic sites provides a valuable insight into protein function. Although many computational methods have been developed to predict catalytic residues and active sites, their accuracy remains low, with a significant number of false positives. In this paper, we present a novel method for the prediction of catalytic sites, using a carefully selected, supervised machine learning algorithm coupled with an optimal discriminative set of protein sequence conservation and structural properties. RESULTS: To determine the best machine learning algorithm, 26 classifiers in the WEKA software package were compared using a benchmarking dataset of 79 enzymes with 254 catalytic residues in a 10-fold cross-validation analysis. Each residue of the dataset was represented by a set of 24 residue properties previously shown to be of functional relevance, as well as a label {+1/-1} to indicate catalytic/non-catalytic residue. The best-performing algorithm was the Sequential Minimal Optimization (SMO) algorithm, which is a Support Vector Machine (SVM). The Wrapper Subset Selection algorithm further selected seven of the 24 attributes as an optimal subset of residue properties, with sequence conservation, catalytic propensities of amino acids, and relative position on protein surface being the most important features. CONCLUSION: The SMO algorithm with 7 selected attributes correctly predicted 228 of the 254 catalytic residues, with an overall predictive accuracy of more than 86%. Missing only 10.2% of the catalytic residues, the method captures the fundamental features of catalytic residues and can be used as a "catalytic residue filter" to facilitate experimental identification of catalytic residues for proteins with known structure but unknown function