Source Of Structural Information Research Articles

Modeling the Influenza A NP-vRNA-Polymerase Complex in Atomic Detail.

Seasonal flu is an acute respiratory disease that exacts a massive toll on human populations, healthcare systems and economies. The disease is caused by an enveloped Influenza virus containing eight ribonucleoprotein (RNP) complexes. Each RNP incorporates multiple copies of nucleoprotein (NP), a fragment of the viral genome (vRNA), and a viral RNA-dependent RNA polymerase (POL), and is responsible for packaging the viral genome and performing critical functions including replication and transcription. A complete model of an Influenza RNP in atomic detail can elucidate the structural basis for viral genome functions, and identify potential targets for viral therapeutics. In this work we construct a model of a complete Influenza A RNP complex in atomic detail using multiple sources of structural and sequence information and a series of homology-modeling techniques, including a motif-matching fragment assembly method. Our final model provides a rationale for experimentally-observed changes to viral polymerase activity in numerous mutational assays. Further, our model reveals specific interactions between the three primary structural components of the RNP, including potential targets for blocking POL-binding to the NP-vRNA complex. The methods developed in this work open the possibility of elucidating other functionally-relevant atomic-scale interactions in additional RNP structures and other biomolecular complexes.

2D ferrous nitroprussides stabilized through organic molecules as pillars: preparation, crystal structure and related properties

Transition metal (T) nitroprussides form a family of coordination complexes with a wide structural diversity including 3 D, 2 D, 1 D, 0 D and ionic phases. Recently, a reversible thermally induced spin transition has been reported for 2 D ferrous nitroprussides stabilized through pyridine and its derivatives as pillars between neighboring layers. The aim of this study is to explore the possibility of obtaining analog solids with spin-crossover behavior using other molecules as pillars. In this contribution we are reporting the crystal structure, solved and refined from powder XRD data, for the resulting hybrid solids containing 1,3-oxazole, 1H-pyrazole, pyridazine, 3-pyridine-3-carboxaldheyde, imidazo[1,2-a]pyridine, and 4-(2-pyridin-4-ylethyl)pyridine as pillar molecules in the interlayer region. The XRD data were complemented with TG curves, and IR, Raman, Mössbauer spectra, and magnetic measurements as a source of complementary structural information. According to the Mössbauer spectra and magnetic data recorded in the temperature range of 5 to 300 K, no evidence of thermally induced spin-crossover was observed for the 2 D ferrous nitroprussides with these six organic ligands as pillars. Such behavior is in contrast with the one observed for pyridine and its derivatives and it is explained in terms of the local symmetry around the high spin Fe2+ ion, as revealed in the recorded Mössbauer spectra. This suggests that the spin-crossover behavior could be induced by introducing guest molecules, as adsorbed species, in the interlayer region to force to a higher symmetry for the iron ion coordination environment.

Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches.

Covering: up to the end of 2020Recently introduced computational metabolome mining tools have started to positively impact the chemical and biological interpretation of untargeted metabolomics analyses. We believe that these current advances make it possible to start decomposing complex metabolite mixtures into substructure and chemical class information, thereby supporting pivotal tasks in metabolomics analysis including metabolite annotation, the comparison of metabolic profiles, and network analyses. In this review, we highlight and explain key tools and emerging strategies covering 2015 up to the end of 2020. The majority of these tools aim at processing and analyzing liquid chromatography coupled to mass spectrometry fragmentation data. We start with defining what substructures are, how they relate to molecular fingerprints, and how recognizing them helps to decompose complex mixtures. We continue with chemical classes that are based on the presence or absence of particular molecular scaffolds and/or functional groups and are thus intrinsically related to substructures. We discuss novel tools to mine substructures, annotate chemical compound classes, and create mass spectral networks from metabolomics data and demonstrate them using two case studies. We also review and speculate about the opportunities that NMR spectroscopy-based metabolome mining of complex metabolite mixtures offers to discover substructures and chemical classes. Finally, we will describe the main benefits and limitations of the current tools and strategies that rely on them, and our vision on how this exciting field can develop toward repository-scale-sized metabolomics analyses. Complementary sources of structural information from genomics analyses and well-curated taxonomic records are also discussed. Many research fields such as natural products discovery, pharmacokinetic and drug metabolism studies, and environmental metabolomics increasingly rely on untargeted metabolomics to gain biochemical and biological insights. The here described technical advances will benefit all those metabolomics disciplines by transforming spectral data into knowledge that can answer biological questions.

Complexes Formed between Artificial Receptors and β‐Glucopyranoside in the Crystalline State

In contrast to numerous known crystal structures of protein‐carbohydrate complexes, which act as a source of structural information about carbohydrate‐mediated recognition processes, there are only individual literature reports on crystal structures of complexes formed between artificial receptors and sugars. In this context, the new crystalline complexes of acyclic receptors and β‐d‐glucopyranoside described in this article provide particularly valuable model systems to study the basic molecular features of carbohydrate recognition. The detailed analyses of the binding modes observed in these complexes have shown their remarkable similarity to those used by carbohydrate‐binding proteins. It is noteworthy that many of the basic molecular features of protein‐carbohydrate interactions, that have been summarized years ago in some literature reports, apply to interactions observed in complexes formed by the artificial receptors.

Deep fusion of multiple term-similarity measures for biomedical passage retrieval

Passage retrieval is an important stage of question answering systems. Closed domain passage retrieval, e.g. biomedical passage retrieval presents additional challenges such as specialized terminology, more complex and elaborated queries, scarcity in the amount of available data, among others. However, closed domains also offer some advantages such as the availability of specialized structured information sources, e.g. ontologies and thesauri, that could be used to improve retrieval performance. This paper presents a novel approach for biomedical passage retrieval which is able to combine different information sources using a similarity matrix fusion strategy based on convolutional neural network architecture. The method was evaluated over the standard BioASQ dataset, a dataset specialized on biomedical question answering. The results show that the method is an effective strategy for biomedical passage retrieval able to outperform other state-of-the-art methods in this domain.

On the accuracy and precision of X-ray and neutron diffraction results as a function of resolution and the electron density model.

X-ray diffraction is the main source of three-dimensional structural information. In total, more than 1.5 million crystal structures have been refined and deposited in structural databanks (PDB, CSD and ICSD) to date. Almost 99.7% of them were obtained by approximating atoms as spheres within the independent atom model (IAM) introduced over a century ago. In this study, X-ray datasets for single crystals of hydrated α-oxalic acid were refined using several alternative electron density models that abandon the crude spherical approximation: the multipole model (MM), the transferable aspherical atom model (TAAM) and the Hirshfeld atom refinement (HAR) model as a function of the resolution of X-ray data. The aspherical models (MM, TAAM, HAR) give far more accurate and precise single-crystal X-ray results than IAM, sometimes identical to results obtained from neutron diffraction and at low resolution. Hence, aspherical approaches open new routes for improving existing structural information collected over the last century.

Measuring transverse relaxation in highly paramagnetic systems

The enhancement of nuclear relaxation rates due to the interaction with a paramagnetic center (known as Paramagnetic Relaxation Enhancement) is a powerful source of structural and dynamics information, widely used in structural biology. However, many signals affected by the hyperfine interaction relax faster than the evolution periods of common NMR experiments and therefore they are broadened beyond detection. This gives rise to a so-called blind sphere around the paramagnetic center, which is a major limitation in the use of PREs. Reducing the blind sphere is extremely important in paramagnetic metalloproteins. The identification, characterization, and proper structural restraining of the first coordination sphere of the metal ion(s) and its immediate neighboring regions is key to understand their biological function. The novel HSQC scheme we propose here, that we termed R2-weighted, HSQC-AP, achieves this aim by detecting signals that escaped detection in a conventional HSQC experiment and provides fully reliable R2 values in the range of 1H R2 rates ca. 50–400 s−1. Independently on the type of paramagnetic center and on the size of the molecule, this experiment decreases the radius of the blind sphere and increases the number of detectable PREs. Here, we report the validation of this approach for the case of PioC, a small protein containing a high potential 4Fe-4S cluster in the reduced [Fe4S4]2+ form. The blind sphere was contracted to a minimal extent, enabling the measurement of R2 rates for the cluster coordinating residues.

Harmonizing structural mass spectrometry analyses in the mass spec studio

Structural Mass Spectrometry (SMS) provides a comprehensive toolbox for the analysis of protein structure and function. It offers multiple sources of structural information that are increasingly useful for integrative structural modeling of complex protein systems. As MS-based structural workflows scale to larger systems, consistent and coherent data interpretation resources are needed to better support modeling. Unlike the proteomics community, practitioners of SMS lack adequate computational tools. Here, we review new developments in the Mass Spec Studio: an expandable ecosystem of workflows for the analysis of complementary SMS techniques with linkages to modeling. Current functionality in the Studio (version 2) supports three major SMS workflows (crosslinking, hydrogen/deuterium exchange and covalent labelling) and two pipelines for structural modeling, with a special focus on data integration. The Mass Spec Studio is an architecture focused on rapid and robust extension of functionality by a community of developers. SignificanceThis review surveys the new data analysis capabilities within the Mass Spec Studio, a rich framework for rapid software development specifically targeting the community of structural proteomics and structural mass spectrometry. Updates to crosslinking, hydrogen/deuterium-exchange and covalent labeling apps are provided as well as a utility for translating such analyses into restraints that support integrative structural modeling. These new capabilities, together with the underlying design tools and content, provide the community with a wealth of resources to tackle complex structural problem and design new approaches to data analysis.

An Insight into the Synthesis and SAR of 2,4-Thiazolidinediones (2,4-TZD) as Multifunctional Scaffold: A Review.

2,4-Thiazolidinedione (2,4-TZD) is a versatile pharmacophore, a privileged scaffold, and a remarkable sulphur-containing heterocyclic compound with diverse pharmacological activities. The multifarious biological activities, due to different mechanisms of action, low cost, and easy availability of 2,4-TZD impressed medicinal chemists to integrate this moiety to develop various lead compounds with diverse therapeutic actions. This resulted in the swift development in the last decade for generating different new potential molecules bearing 2,4-TZD. In this review, the authors attempt to shape and present the latest investigations (2012 onwards) going on in generating promising 2,4-TZD containing lead compounds. The data has been collected and analyzed to develop the structure-activity relationship (SAR). The SAR and active pharmacophores of various leads accountable for antidiabetic, anticancer, antimicrobial, and antioxidant activities have also been illustrated. This review also highlighted some of the important chemical synthetic routes for the preparation of various 2,4-TZD derivatives. This review will definitely serve as a useful source of structural information to medicinal chemists and may be utilized for the strategic design of potent 2,4-TZD derivatives in the future.

A novel high-dimensional NMR experiment for resolving protein backbone dihedral angle ambiguities

Intrinsically disordered proteins (IDPs) are challenging established structural biology perception and urge a reassessment of the conventional understanding of the subtle interplay between protein structure and dynamics. Due to their importance in eukaryotic life and central role in protein interaction networks, IDP research is a fascinating and highly relevant research area in which NMR spectroscopy is destined to be a key player. The flexible nature of IDPs, as a result of the sampling of a vast conformational space, however, poses a tremendous scientific challenge, both technically and theoretically. Pronounced signal averaging results in narrow signal dispersion and requires higher dimensionality NMR techniques. Moreover, a fundamental problem in the structural characterization of IDPs is the definition of the conformational ensemble sampled by the polypeptide chain in solution, where often the interpretation relies on the concept of ‘residual structure’ or ‘conformational preference’. An important source of structural information is information-rich NMR experiments that probe protein backbone dihedral angles in a unique manner. Cross-correlated relaxation experiments have proven to fulfil this task as they provide unique information about protein backbones, particularly in IDPs. Here we present a novel cross-correlation experiment that utilizes non-uniform sampling detection schemes to resolve protein backbone dihedral ambiguities in IDPs. The sensitivity of this novel technique is illustrated with an application to the prototypical IDP alpha-Synculein for which unexpected deviations from random-coil-like behaviour could be observed.

MurD inhibitors as antibacterial agents: a review

Peptidoglycan is an essential component of the bacterial cell wall and is required for the survival of bacteria. ATP-dependent MurC–F ligases are responsible for the early stages of peptidoglycan biosynthesis. Second in the series, MurD catalyzes the addition of d-glutamic acid to UDP-MurNAc-l-Ala which involves acyl-phosphate and tetrahedral intermediates. Due to its high specificity for d-glutamic acid substrate and its absence in mammalian cells, MurD is considered as an attractive target for the design of novel antibacterial agents. Several MurD inhibitors have been designed and tested for their activity. These include phosphinates, N-acetylmuramic acids, pulvinones, phosphinodipeptide, cyclic nonapeptides, macrocyclic compounds, benzene-1,3-dicarboxylic acid, naphthalene sulphonamides, 2-thioxothiazolidin-4-ones, benzylidene-2,4-thiazolidindiones, etc. In the present review, an updated status of MurD enzyme inhibitors is presented which will serve as a useful source of structural information and may be utilized for the design of potent inhibitors against this enzyme.

Advantages of Colour Etching in Quality Control of Recycled Al-Si Cast Alloys

Abstract Colour Metallography is a set of light metallographic microscopy methodologies that utilize phase colour contrast as a source of new structure information compared to conventional techniques. In many cases, colour contrast can be induced where no identifiable phase contrast is obtained by conventional techniques. With the help of colour contrast, we gain new information on the structure of Al-Si alloys, especially intermetallic phases, in which the benefits of its use in casting quality control are of benefit. Blue-yellow contrast is definitely more pronounced than dark grey or light grey contrast. There is no substantial reproducible colour reproduction, since it is irrelevant whether the two phases are distinguished by blue-yellow or green-ocher contrast. When using the colour contrast, the structural components differ not only according to their microscopic appearance, but also based on the knowledge of the chemical composition of alloys, interactions between structural components, knowledge of the possible influence of Al-alloys structure (e.g. refining, modifications, heat treatment). Colour contrast in quality control of Al-Si alloys was applied to AlSiMg cast alloy (AlSi7Mg0.3; AlSi7MgTi; AlSi10MgMn; etc.)

Implementación de un sistema de detección de señales débiles de futuro mediante técnicas de minería de textos

Actualmente, una de las mayores amenazas para las empresas es no ser capaces de hacer frente a los cambios constantes que se dan en el mercado, por no predecirlos con la suficiente antelación. Por ello, el desarrollo de nuevos procesos que faciliten la detección de fenómenos y cambios futuros significativos es una componente clave para una correcta toma de decisiones que marque un rumbo correcto para la empresa. Por esta razón, se propone un sistema basado en una arquitectura de inteligencia de negocio que permite detectar cambios discretos o señales débiles (weak signals) en el presente, pero que son indicativos de fenómenos más significativos y cambios trascendentales en el futuro. Frente a los trabajos actuales que se centran en fuentes de información estructuradas, o como mucho, con un único tipo de fuente de datos, en este trabajo la detección de estas señales se realiza de forma cuantitativa a partir de documentos heterogéneos y no estructurados de diversa índole (artículos científicos, periodísticos y redes sociales) sobre los que se aplican técnicas de minería de textos. El sistema ha sido testeado para estudiar el futuro del sector de los paneles solares, habiéndose obtenido resultados prometedores para ayudar a expertos en el reconocimiento de nuevos factores de peso en sus mercados y en el desarrollo de nuevas oportunidades.

Multi-fidelity Kriging-assisted structural optimization of whole engine models employing medial meshes

Finite element models of whole gas turbine engines, also known as whole engine models (WEMs), which consist of three-dimensional solid elements are not commonly used in design optimization studies due to the high computational cost of solving them for many designs. WEMs consisting of two-dimensional shell elements can be a suitable replacement for high-fidelity solid WEMs as they approximate the responses well while being significantly quicker to solve. However, in a surrogate-assisted optimization study, the accumulation of errors in the shell WEM evaluations can result in the construction of a surrogate model that can be somewhat misleading compared to the solid WEM response surface. Such a surrogate model could return promising designs that, when validated using solid WEMs, turn out to be suboptimal or infeasible. A novel approach which combines medial meshing and multi-fidelity surrogate modelling techniques is proposed to increase the feasibility of conducting whole engine optimization studies. We demonstrate the workflow for generating medial meshes on an engine intercasing geometry. The accuracy of medial mesh simulations with respect to solid mesh simulations is evaluated and discussed in the context of their suitability as a source of low-fidelity structural information for multi-fidelity surrogate models. The impact of this combination of techniques is subsequently illustrated using two case studies. The first case study is the optimization of an intermediate compressor casing for minimum mass with constraints on the casing stiffness. The results show that the multi-fidelity approach is able to find optimum designs that are equivalent to the expensive single-fidelity approach of using only solid mesh evaluations but at a significantly lower computational cost. The second case study is the optimization of a whole engine geometry. This case study serves to demonstrate the effectiveness of the multi-fidelity approach for solving realistic design problems.

Integrative Structure Modeling: Overview and Assessment.

Integrative structure modeling computationally combines data from multiple sources of information with the aim of obtaining structural insights that are not revealed by any single approach alone. In the first part of this review, we survey the commonly used sources of structural information and the computational aspects of model building. Throughout the past decade, integrative modeling was applied to various biological systems, with a focus on large protein complexes. Recent progress in the field of cryo-electron microscopy (cryo-EM) has resolved many of these complexes to near-atomic resolution. In the second part of this review, we compare a range of published integrative models with their higher-resolution counterparts with the aim of critically assessing their accuracy. This comparison gives a favorable view of integrative modeling and demonstrates its ability to yield accurate and informative results. We discuss possible roles of integrative modeling in the new era of cryo-EM and highlight future challenges and directions.

Learning IP network representations

We present DIP, a deep learning based framework to learn structural properties of the Internet, such as node clustering or distance between nodes. Existing embedding-based approaches use linear algorithms on a single source of data, such as latency or hop count information, to approximate the position of a node in the Internet. In contrast, DIP computes low-dimensional representations of nodes that preserve structural properties and non-linear relationships across multiple, heterogeneous sources of structural information, such as IP, routing, and distance information. Using a large real-world data set, we show that DIP learns representations that preserve the real-world clustering of the associated nodes and predicts distance between them more than 30% better than a meanbased approach. Furthermore, DIP accurately imputes hop count distance to unknown hosts (i.e., not used in training) given only their IP addresses and routable prefixes. Our framework is extensible to new data sources and applicable to a wide range of problems in network monitoring and security.

Conformational Equilibrium of Cinchonidine in C6D12 Solution. Alternative NMR/DFT Approach.

1H NMR and 13C NMR chemical shifts as well as conformation dependent vicinal 1H-1H spin-spin coupling constants for cinchonidine in a dilute C6D12 solution have been measured. These data have been interpreted in detail exploiting the results of the extensive quantum chemistry calculations of molecular geometry and NMR parameters of the molecule, performed using the density functional theory (DFT) B3LYP/6-311++G(2d,p) polarizable continuum model (PCM) level of theory. The experimental values of NMR parameters for cinchonidine have been reproduced very well in terms of parameters calculated for key conformers of this molecule. Simultaneously, the analysis has provided us with a lot of information on conformational equilibrium of cinchonidine in the investigated solution. These findings remain in general agreement with the conclusions of other works, based on NOESY spectra or other physicochemical data. Thus, a careful quantitative interpretation of easily measurable NMR chemical shifts can be an independent and valuable source of structural information even in such complex cases as cinchonidine in solution.

Clustering approaches for visual knowledge exploration in molecular interaction networks

BackgroundBiomedical knowledge grows in complexity, and becomes encoded in network-based repositories, which include focused, expert-drawn diagrams, networks of evidence-based associations and established ontologies. Combining these structured information sources is an important computational challenge, as large graphs are difficult to analyze visually.ResultsWe investigate knowledge discovery in manually curated and annotated molecular interaction diagrams. To evaluate similarity of content we use: i) Euclidean distance in expert-drawn diagrams, ii) shortest path distance using the underlying network and iii) ontology-based distance. We employ clustering with these metrics used separately and in pairwise combinations. We propose a novel bi-level optimization approach together with an evolutionary algorithm for informative combination of distance metrics. We compare the enrichment of the obtained clusters between the solutions and with expert knowledge. We calculate the number of Gene and Disease Ontology terms discovered by different solutions as a measure of cluster quality.Our results show that combining distance metrics can improve clustering accuracy, based on the comparison with expert-provided clusters. Also, the performance of specific combinations of distance functions depends on the clustering depth (number of clusters). By employing bi-level optimization approach we evaluated relative importance of distance functions and we found that indeed the order by which they are combined affects clustering performance.Next, with the enrichment analysis of clustering results we found that both hierarchical and bi-level clustering schemes discovered more Gene and Disease Ontology terms than expert-provided clusters for the same knowledge repository. Moreover, bi-level clustering found more enriched terms than the best hierarchical clustering solution for three distinct distance metric combinations in three different instances of disease maps.ConclusionsIn this work we examined the impact of different distance functions on clustering of a visual biomedical knowledge repository. We found that combining distance functions may be beneficial for clustering, and improve exploration of such repositories. We proposed bi-level optimization to evaluate the importance of order by which the distance functions are combined. Both combination and order of these functions affected clustering quality and knowledge recognition in the considered benchmarks. We propose that multiple dimensions can be utilized simultaneously for visual knowledge exploration.

Deep multi-task learning for a geographically-regularized semantic segmentation of aerial images

When approaching the semantic segmentation of overhead imagery in the decimeter spatial resolution range, successful strategies usually combine powerful methods to learn the visual appearance of the semantic classes (e.g. convolutional neural networks) with strategies for spatial regularization (e.g. graphical models such as conditional random fields).In this paper, we propose a method to learn evidence in the form of semantic class likelihoods, semantic boundaries across classes and shallow-to-deep visual features, each one modeled by a multi-task convolutional neural network architecture. We combine this bottom-up information with top-down spatial regularization encoded by a conditional random field model optimizing the label space across a hierarchy of segments with constraints related to structural, spatial and data-dependent pairwise relationships between regions.Our results show that such strategy provide better regularization than a series of strong baselines reflecting state-of-the-art technologies. The proposed strategy offers a flexible and principled framework to include several sources of visual and structural information, while allowing for different degrees of spatial regularization accounting for priors about the expected output structures.

13Check_RNA: a tool to evaluate 13C chemical shift assignments of RNA.

Chemical shifts (CS) are an important source of structural information of macromolecules such as RNA. In addition to the scarce availability of CS for RNA, the observed values are prone to errors due to a wrong re-calibration or miss assignments. Different groups have dedicated their efforts to correct CS systematic errors on RNA. Despite this, there are not automated and freely available algorithms for evaluating the referencing of RNA 13 C CS before their deposition to the BMRB or re-reference already deposited CS with systematic errors. Based on an existent method we have implemented an open source python module to correct 13 C CS (from here on 13Cexp) systematic errors of RNAs and then return the results in 3 formats including the nmrstar one. This software is available on GitHub at https://github.com/BIOS-IMASL/13Check_RNA under a MIT license. Supplementary data are available at Bioinformatics online.