Paleoproterozoic Geomagnetic Field Strength From the Avanavero Mafic Sills, Amazonian Craton, Brazil

A recent hypothesis has suggested that Earth's inner core nucleated during the Mesoproterozoic, as evidenced by a rapid increase in the paleointensity (ancient geomagnetic field intensity) record; however, paleointensity data during the Paleoproterozoic and Mesoproterozoic period are limited. To address this problem, we have determined paleointensity from samples from three Paleoproterozoic Avanavero mafic sills (Amazonian Craton, Brazil): Cotingo, 1782 Ma, Puiuà 1788, and Pedra Preta, 1795 Ma. We adopted a multi-protocol approach for paleointensity estimates combining Thellier-type IZZI and LTD-IZZI methods, and the non-heating Preisach protocol. We obtained an average VDM value of 1.3 ± 0.7 × 1022Am2 (Cotingo) of 2.0 ± 0.4 × 1022Am2 (Puiuà) and 6 ± 4 × 1022Am2 (Pedra Preta); it is argued that the Cotingo estimate is the most robust. Our results are the first data from the upper Paleoproterozoic for South America and are comparable to data available from other regions and similar periods. The new data do not invalidate the hypothesis of that Earth's inner core nucleated during the Mesoproterozoic

High-throughput isolation and characterization of untagged membrane protein complexes: outer membrane complexes of Desulfovibrio vulgaris.

Cell membranes represent the "front line" of cellular defense and the interface between a cell and its environment. To determine the range of proteins and protein complexes that are present in the cell membranes of a target organism, we have utilized a "tagless" process for the system-wide isolation and identification of native membrane protein complexes. As an initial subject for study, we have chosen the Gram-negative sulfate-reducing bacterium Desulfovibrio vulgaris. With this tagless methodology, we have identified about two-thirds of the outer membrane- associated proteins anticipated. Approximately three-fourths of these appear to form homomeric complexes. Statistical and machine-learning methods used to analyze data compiled over multiple experiments revealed networks of additional protein-protein interactions providing insight into heteromeric contacts made between proteins across this region of the cell. Taken together, these results establish a D. vulgaris outer membrane protein data set that will be essential for the detection and characterization of environment-driven changes in the outer membrane proteome and in the modeling of stress response pathways. The workflow utilized here should be effective for the global characterization of membrane protein complexes in a wide range of organisms

JGromacs: A Java Package for Analyzing Protein Simulations

UNLABELLED: In this paper, we introduce JGromacs, a Java API (Application Programming Interface) that facilitates the development of cross-platform data analysis applications for Molecular Dynamics (MD) simulations. The API supports parsing and writing file formats applied by GROMACS (GROningen MAchine for Chemical Simulations), one of the most widely used MD simulation packages. JGromacs builds on the strengths of object-oriented programming in Java by providing a multilevel object-oriented representation of simulation data to integrate and interconvert sequence, structure, and dynamics information. The easy-to-learn, easy-to-use, and easy-to-extend framework is intended to simplify and accelerate the implementation and development of complex data analysis algorithms. Furthermore, a basic analysis toolkit is included in the package. The programmer is also provided with simple tools (e.g., XML-based configuration) to create applications with a user interface resembling the command-line interface of GROMACS applications. AVAILABILITY: JGromacs and detailed documentation is freely available from http://sbcb.bioch.ox.ac.uk/jgromacs under a GPLv3 license

Quantifying Water-Mediated Protein–Ligand Interactions in a Glutamate Receptor: A DFT Study

It is becoming increasingly clear that careful treatment of water molecules in ligand–protein interactions is required in many cases if the correct binding pose is to be identified in molecular docking. Water can form complex bridging networks and can play a critical role in dictating the binding mode of ligands. A particularly striking example of this can be found in the ionotropic glutamate receptors. Despite possessing similar chemical moieties, crystal structures of glutamate and α-amino-3-hydroxy-5-methyl-4-isoxazole-propionic acid (AMPA) in complex with the ligand-binding core of the GluA2 ionotropic glutamate receptor revealed, contrary to all expectation, two distinct modes of binding. The difference appears to be related to the position of water molecules within the binding pocket. However, it is unclear exactly what governs the preference for water molecules to occupy a particular site in any one binding mode. In this work we use density functional theory (DFT) calculations to investigate the interaction energies and polarization effects of the various components of the binding pocket. Our results show (i) the energetics of a key water molecule are more favorable for the site found in the glutamate-bound mode compared to the alternative site observed in the AMPA-bound mode, (ii) polarization effects are important for glutamate but less so for AMPA, (iii) ligand–system interaction energies alone can predict the correct binding mode for glutamate, but for AMPA alternative modes of binding have similar interaction energies, and (iv) the internal energy is a significant factor for AMPA but not for glutamate. We discuss the results within the broader context of rational drug-design

Nonparametric identification of regulatory interactions from spatial and temporal gene expression data

<p>Abstract</p> <p>Background</p> <p>The correlation between the expression levels of transcription factors and their target genes can be used to infer interactions within animal regulatory networks, but current methods are limited in their ability to make correct predictions.</p> <p>Results</p> <p>Here we describe a novel approach which uses nonparametric statistics to generate ordinary differential equation (ODE) models from expression data. Compared to other dynamical methods, our approach requires minimal information about the mathematical structure of the ODE; it does not use qualitative descriptions of interactions within the network; and it employs new statistics to protect against over-fitting. It generates spatio-temporal maps of factor activity, highlighting the times and spatial locations at which different regulators might affect target gene expression levels. We identify an ODE model for <it>eve </it>mRNA pattern formation in the <it>Drosophila melanogaster </it>blastoderm and show that this reproduces the experimental patterns well. Compared to a non-dynamic, spatial-correlation model, our ODE gives 59% better agreement to the experimentally measured pattern. Our model suggests that protein factors frequently have the potential to behave as both an activator and inhibitor for the same <it>cis</it>-regulatory module depending on the factors' concentration, and implies different modes of activation and repression.</p> <p>Conclusions</p> <p>Our method provides an objective quantification of the regulatory potential of transcription factors in a network, is suitable for both low- and moderate-dimensional gene expression datasets, and includes improvements over existing dynamic and static models.</p

Binding Site Turnover Produces Pervasive Quantitative Changes in Transcription Factor Binding between Closely Related Drosophila Species

Genome-wide comparison of transcription factor binding between related Drosophila species highlights how sequence changes affect the biochemical events that underlie animal development

Rapid and Accurate Prediction and Scoring of Water Molecules in Protein Binding Sites

Water plays a critical role in ligand-protein interactions. However, it is still challenging to predict accurately not only where water molecules prefer to bind, but also which of those water molecules might be displaceable. The latter is often seen as a route to optimizing affinity of potential drug candidates. Using a protocol we call WaterDock, we show that the freely available AutoDock Vina tool can be used to predict accurately the binding sites of water molecules. WaterDock was validated using data from X-ray crystallography, neutron diffraction and molecular dynamics simulations and correctly predicted 97% of the water molecules in the test set. In addition, we combined data-mining, heuristic and machine learning techniques to develop probabilistic water molecule classifiers. When applied to WaterDock predictions in the Astex Diverse Set of protein ligand complexes, we could identify whether a water molecule was conserved or displaced to an accuracy of 75%. A second model predicted whether water molecules were displaced by polar groups or by non-polar groups to an accuracy of 80%. These results should prove useful for anyone wishing to undertake rational design of new compounds where the displacement of water molecules is being considered as a route to improved affinity

Playing is Performing – Video Games as Performance

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