FindGeo: a tool for determining metal coordination geometry.

Abstract Summary: Metals are essential for the structure and function of many proteins and nucleic acids. The geometrical arrangement of the atoms that coordinate a metal in a biological macromolecule is an important determinant of the specificity and role of that metal. At present, however, this information can be retrieved only from the literature, which sometimes contains an improper or incorrect description of the geometry, and often lacks it altogether. Thus, we developed FindGeo to quickly and easily determine the coordination geometry of selected, or all, metals in a given structure. FindGeo works by superimposing the metal-coordinating atoms in the input structure to a library of templates with alternative ideal geometries, which are ranked by RMSD to identify the best geometry assignment. Availability: FindGeo is freely available as a web service and as a stand-alone program at http://metalweb.cerm.unifi.it/tools/findgeo/. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

Remote Sensing Data Analytics with the Udocker Container Tool using Multi-GPU Deep Learning Systems

Multi-GPU systems are in continuous development to deal with the challenges of intensive computational big data problems. On the one hand, parallel architectures provide a tremendous computation capacity and outstanding scalability. On the other hand, the production path in multi-user environments faces several roadblocks since they do not grant root privileges to the users. Containers provide flexible strategies for packing, deploying and running isolated application processes within multi-user systems and enable scientific reproducibility. This paper describes the usage and advantages that the uDocker container tool offers for the development of deep learning models in the described context. The experimental results show that uDocker is more transparent to deploy for less tech-savvy researchers and allows the application to achieve processing time with negligible overhead compared to an uncontainerized environment

MetalPDB: a database of metal sites in biological macromolecular structures

We present here MetalPDB (freely accessible a

Molecular Dynamics Characterization of the C2 Domain of Protein Kinase Cβ

Protein kinase C (PKC) isozymes comprise a family of related enzymes that play a central role in many intracellular eukaryotic signaling events. Isozyme specificity is mediated by association of each PKC isozyme with specific anchoring proteins, termed RACKs. The C2 domain of betaPKC contains at least part of the RACK-binding sites. Because the C2 domain contains also a RACK-like sequence (termed pseudo-RACK), it was proposed that this pseudo-RACK site mediates intramolecular interaction with one of the RACK-binding sites in the C2 domain itself, stabilizing the inactive conformation of betaPKC. BetaPKC depends on calcium for its activation, and the C2 domain contains the calcium-binding sites. The x-ray structure of the C2 domain of betaPKC shows that three Ca(2+) ions can be coordinated by two opposing loops at one end of the domain. Starting from this x-ray structure, we have performed molecular dynamics (MD) calculations on the C2 domain of betaPKC bound to three Ca(2+) ions, to two Ca(2+) ions, and in the Ca(2+)-free state, in order to analyze the effect of calcium on the RACK-binding sites and the pseudo-RACK sites, as well as on the loops that constitute the binding site for the Ca(2+) ions. The results show that calcium stabilizes the beta-sandwich structure of the C2 domain and thus affects two of the three RACK-binding sites within the C2 domain. Also, the interactions between the third RACK-binding site and the pseudo-RACK site are not notably modified by the removal of Ca(2+) ions. On that basis, we predict that the pseudo-RACK site within the C2 domain masks a RACK-binding site in another domain of betaPKC, possibly the V5 domain. Finally, the MD modeling shows that two Ca(2+) ions are able to interact with two molecules of O-phospho-l-serine. These data suggest that Ca(2+) ions may be directly involved in PKC binding to phosphatidylserine, an acidic lipid located exclusively on the cytoplasmic face of membranes, that is required for PKC activation

Extended Self-Dual Attribute Profiles for the Classification of Hyperspectral Images

International audience—In this letter, we explore the use of self-dual attribute profiles (SDAPs) for the classification of hyperspectral images. The hyperspectral data are reduced into a set of components by non-parametric weighted feature extraction (NWFE), and a morphological processing is then performed by the SDAPs separately on each of the extracted components. Since the spatial information extracted by SDAPs results in a high number of features, the NWFE is applied a second time in order to extract a fixed number of features, which are finally classified. The experiments are carried out on two hyperspectral images, and the support vector machines (SVMs) and Random Forest (RF) are used as classifiers. The effectiveness of SDAPs is assessed by comparing its results against those obtained by an approach based on extended attribute profiles (EAPs)

Remote Sensing Image Classification Using Attribute Filters Defined over the Tree of Shapes

International audience—Remotely sensed images with very high spatial resolution provide a detailed representation of the surveyed scene with a geometrical resolution that at the present can be up to 30 cm (WorldView-3). A set of powerful image processing operators have been defined in the mathematical morphology framework. Among those, connected operators (e.g., attribute filters) have proven their effectiveness in processing very high resolution images. Attribute filters are based on attributes which can be efficiently implemented on tree-based image representations. In this work, we considered the definition of min, max, direct and subtractive filter rules for the computation of attribute filters over the tree of shapes representation. We study their performance on the classification of remotely sensed images. We compare the classification results over the tree of shapes with the results obtained when the same rules are applied on the component trees. The random forest is used as a baseline classifier and the experiments are conducted using multispectral data sets acquired by QuickBird and IKONOS sensors over urban areas

On Understanding Big Data Impacts in Remotely Sensed Image Classification Using Support Vector Machine Methods

Owing to the recent development of sensor resolutions onboard different Earth observation platforms, remote sensing is an important source of information for mapping and monitoring natural and man-made land covers. Of particular importance is the increasing amounts of available hyperspectral data originating from airborne and satellite sensors such as AVIRIS, HyMap, and Hyperion with very high spectral resolution (i.e., high number of spectral channels) containing rich information for a wide range of applications. A relevant example is the separation of different types of land-cover classes using the data in order to understand, e.g., impacts of natural disasters or changing of city buildings over time. More recently, such increases in the data volume, velocity, and variety of data contributed to the term big data that stand for challenges shared with many other scientific disciplines. On one hand, the amount of available data is increasing in a way that raises the demand for automatic data analysis elements since many of the available data collections are massively underutilized lacking experts for manual investigation. On the other hand, proven statistical methods (e.g., dimensionality reduction) driven by manual approaches have a significant impact in reducing the amount of big data toward smaller smart data contributing to the more recently used terms data value and veracity (i.e., less noise, lower dimensions that capture the most important information). This paper aims to take stock of which proven statistical data mining methods in remote sensing are used to contribute to smart data analysis processes in the light of possible automation as well as scalable and parallel processing techniques. We focus on parallel support vector machines (SVMs) as one of the best out-of-the-box classification methods.Sponsored by: IEEE Geoscience & Remote Sensing SocietyRitrýnt tímaritPeer reviewedPre prin

Deep-Learning-Based 3-D Surface Reconstruction—A Survey

In the last decade, deep learning (DL) has significantly impacted industry and science. Initially largely motivated by computer vision tasks in 2-D imagery, the focus has shifted toward 3-D data analysis. In particular, 3-D surface reconstruction, i.e., reconstructing a 3-D shape from sparse input, is of great interest to a large variety of application fields. DL-based approaches show promising quantitative and qualitative surface reconstruction performance compared to traditional computer vision and geometric algorithms. This survey provides a comprehensive overview of these DL-based methods for 3-D surface reconstruction. To this end, we will first discuss input data modalities, such as volumetric data, point clouds, and RGB, single-view, multiview, and depth images, along with corresponding acquisition technologies and common benchmark datasets. For practical purposes, we also discuss evaluation metrics enabling us to judge the reconstructive performance of different methods. The main part of the document will introduce a methodological taxonomy ranging from point- and mesh-based techniques to volumetric and implicit neural approaches. Recent research trends, both methodological and for applications, are highlighted, pointing toward future developments