Large Amount Of Structural Information Research Articles

A workflow for deriving chemical entities from crystallographic data and its application to the Crystallography Open Database

Knowledge about the 3-dimensional structure, orientation and interaction of chemical compounds is important in many areas of science and technology. X-ray crystallography is one of the experimental techniques capable of providing a large amount of structural information for a given compound, and it is widely used for characterisation of organic and metal-organic molecules. The method provides precise 3D coordinates of atoms inside crystals, however, it does not directly deliver information about certain chemical characteristics such as bond orders, delocalization, charges, lone electron pairs or lone electrons. These aspects of a molecular model have to be derived from crystallographic data using refined information about interatomic distances and atom types as well as employing general chemical knowledge. This publication describes a curated automatic pipeline for the derivation of chemical attributes of molecules from crystallographic models. The method is applied to build a catalogue of chemical entities in an open-access crystallographic database, the Crystallography Open Database (COD). The catalogue of such chemical entities is provided openly as a derived database. The content of this catalogue and the problems arising in the fully automated pipeline are discussed, along with the possibilities to introduce manual data curation into the process.

Antigenic Structural Similarity as a Predictor for Antibody Cross-Reactivity

Antibodies are an essential component of the adaptive immune system that function to neutralize foreign invaders, such as bacterial and parasitic pathogens. However, B-cell epitopes remain difficult to predict due to their general indistinguishability from other protein regions. Epitope prediction tools in the past have largely relied on amino acid sequence similarity; however, implementing three-dimensional protein structure analyses into the epitope prediction algorithms has been shown to increase detection accuracy. Furthermore, structural comparisons between antigenic proteins for their potential to bind cross-reactive antibodies have not been explored extensively in the literature. Recent studies have pointed to the utility of looking at shared epitope structures in predicting antibody crossreactivity, which may shed light on cross-immunity between infectious pathogens and autoimmune diseases induced after infection. Thus, herein, the potential impact of including structural similarity comparisons in detecting shared epitopes is discussed. With the large amount of structural information being determined by three-dimensional computational protein modelling methods, the ability to perform these analyses is becoming more feasible.

A non-binary hierarchical tree overlapping community detection based on multi-dimensional similarity

Overlapping communities exist in real networks, where the communities represent hierarchical community structures, such as schools and government departments. A non-binary tree allows a vertex to belong to multiple communities to obtain a more realistic overlapping community structure. It is challenging to select appropriate leaf vertices and construct a hierarchical tree that considers a large amount of structural information. In this paper, we propose a non-binary hierarchical tree overlapping community detection based on multi-dimensional similarity. The multi-dimensional similarity fully considers the local structure characteristics between vertices to calculate the similarity between vertices. First, we construct a similarity matrix based on the first and second-order neighbor vertices and select a leaf vertex. Second, we expand the leaf vertex based on the principle of maximum community density and construct a non-binary tree. Finally, we choose the layer with the largest overlapping modularity as the result of community division. Experiments on real-world networks demonstrate that our proposed algorithm is superior to other representative algorithms in terms of the quality of overlapping community detection.

Enabling a Mobile Robot for Autonomous RFID-Based Inventory by Multilayer Mapping and ACO-Enhanced Path Planning

Paper presents a novel application for an autonomous robot to perform RFID-based inventory in a retail environment. For this application, one challenge is to represent a complicated environment by a good quality map. LIDAR (light detection and ranging) sensors only generate a 2D plane map that loses a large amount of structural information. In contrast, stereo or RGB-D cameras provide abundant environmental information but in a limited field of view (FOV), which limits the robot’s ability to gain reliable poses. Another challenge is effectively counting inventory within a massive retail environment; the robot needs to navigate in an optimal route that covers the entire target area.

Characterizing relative degrees of clumping structure in vegetation canopy using waveform LiDAR

With the advances in complexity and resolution in ecological modeling, there is an increasing demand for more detailed characterization of vegetation canopy architecture. Clumping, a characteristic of foliage distribution, is one of the important aspects of canopy structure and significantly influences light penetration through the canopy. Airborne full-waveform LiDAR data contains large amounts of structural information, and presents a powerful avenue for providing detailed foliage distribution information over large areas. In this study, we present a method for describing physical canopy clumping structure for individual trees that can resolve fine scale variations in foliage clumping. We first utilize K-means clustering algorithm to extract structure found within full-waveform LiDAR data. Then we use representative traits for each data cluster to classify them into their natural groups. Based on these traits, we draw conclusions about physical representations of each group and identify groups as containing clumps in vegetation ranging from spares to dense. We formulate a clumping index multiplier based on cluster traits that inform relative degrees of vegetation clumping at the sub-canopy scale. We use the clumping index multiplier to introduce heterogeneity to a modeled plot level clumping index and generate a vertical clumping index profile for a forested plot. This study demonstrates that large amounts of canopy structural information can be extracted from waveform LiDAR data. The fine resolution canopy clumping structure found by the method described in this work can be used as valuable input for ecological models.

Terrestrial laser scanning to quantify above-ground biomass of structurally complex coastal wetland vegetation

Above-ground biomass represents a small yet significant contributor to carbon storage in coastal wetlands. Despite this, above-ground biomass is often poorly quantified, particularly in areas where vegetation structure is complex. Traditional methods for providing accurate estimates involve harvesting vegetation to develop mangrove allometric equations and quantify saltmarsh biomass in quadrats. However broad scale application of these methods may not capture structural variability in vegetation resulting in a loss of detail and estimates with considerable uncertainty. Terrestrial laser scanning (TLS) collects high resolution three-dimensional point clouds capable of providing detailed structural morphology of vegetation. This study demonstrates that TLS is a suitable non-destructive method for estimating biomass of structurally complex coastal wetland vegetation. We compare volumetric models, 3-D surface reconstruction and rasterised volume, and point cloud elevation histogram modelling techniques to estimate biomass. Our results show that current volumetric modelling approaches for estimating TLS-derived biomass are comparable to traditional mangrove allometrics and saltmarsh harvesting. However, volumetric modelling approaches oversimplify vegetation structure by under-utilising the large amount of structural information provided by the point cloud. The point cloud elevation histogram model presented in this study, as an alternative to volumetric modelling, utilises all of the information within the point cloud, as opposed to sub-sampling based on specific criteria. This method is simple but highly effective for both mangrove (r2 = 0.95) and saltmarsh (r2 > 0.92) vegetation. Our results provide evidence that application of TLS in coastal wetlands is an effective non-destructive method to accurately quantify biomass for structurally complex vegetation.

Bridge Structure Deformation Prediction Based on GNSS Data Using Kalman-ARIMA-GARCH Model.

Bridges are an essential part of the ground transportation system. Health monitoring is fundamentally important for the safety and service life of bridges. A large amount of structural information is obtained from various sensors using sensing technology, and the data processing has become a challenging issue. To improve the prediction accuracy of bridge structure deformation based on data mining and to accurately evaluate the time-varying characteristics of bridge structure performance evolution, this paper proposes a new method for bridge structure deformation prediction, which integrates the Kalman filter, autoregressive integrated moving average model (ARIMA), and generalized autoregressive conditional heteroskedasticity (GARCH). Firstly, the raw deformation data is directly pre-processed using the Kalman filter to reduce the noise. After that, the linear recursive ARIMA model is established to analyze and predict the structure deformation. Finally, the nonlinear recursive GARCH model is introduced to further improve the accuracy of the prediction. Simulation results based on measured sensor data from the Global Navigation Satellite System (GNSS) deformation monitoring system demonstrated that: (1) the Kalman filter is capable of denoising the bridge deformation monitoring data; (2) the prediction accuracy of the proposed Kalman-ARIMA-GARCH model is satisfactory, where the mean absolute error increases only from 3.402 mm to 5.847 mm with the increment of the prediction step; and (3) in comparision to the Kalman-ARIMA model, the Kalman-ARIMA-GARCH model results in superior prediction accuracy as it includes partial nonlinear characteristics (heteroscedasticity); the mean absolute error of five-step prediction using the proposed model is improved by 10.12%. This paper provides a new way for structural behavior prediction based on data processing, which can lay a foundation for the early warning of bridge health monitoring system based on sensor data using sensing technology.

Assessing heterogeneity in oligomeric AAA+machines.

ATPases Associated with various cellular Activities (AAA+ATPases) are molecular motors that use the energy of ATP binding and hydrolysis to remodel their target macromolecules. The majority of these ATPases form ring-shaped hexamers in which the active sites are located at the interfaces between neighboring subunits. Structural changes initiate in an active site and propagate to distant motor parts that interface and reshape the target macromolecules, thereby performing mechanical work. During the functioning cycle, the AAA+motor transits through multiple distinct states. Ring architecture and placement of the catalytic sites at the intersubunit interfaces allow for a unique level of coordination among subunits of the motor. This in turn results in conformational differences among subunits and overall asymmetry of the motor ring as it functions. To date, a large amount of structural information has been gathered for different AAA+motors, but even for the most characterized of them only a few structural states are known and the full mechanistic cycle cannot be yet reconstructed. Therefore, the first part of this work will provide a broad overview of what arrangements of AAA+subunits have been structurally observed focusing on diversity of ATPase oligomeric ensembles and heterogeneity within the ensembles. The second part of this review will concentrate on methods that assess structural and functional heterogeneity among subunits of AAA+motors, thus bringing us closer to understanding the mechanism of these fascinating molecular motors.

Frank Allen (1944–2014), member of the Editorial Board of Structural Chemistry

Frank Allen was a crystallographer with a PhD degree from Imperial College (London). Following a postdoctoral stint in Vancouver he returned to his native England and in 1970 he joined the Cambridge Crystallographic Data Centre. He rose to the position of Scientific Director and Executive Director of the Centre. He was a pioneer in data management and data mining. He retired in 2008 but did not retire from crystallography. Frank was for many years member of our Editorial Board. It was reassuring to know that we could turn to him for reviewing manuscripts in which the authors used large amounts of structural information for making conclusions. He was gentle but rigorous in his criticism, always constructive. I have known Frank for decades, first from the literature, later we met at conferences and the Erice schools, and corresponded about journal business. Our field of structural science will miss him; our journal and our community will miss him. Frank Allen (photo by I. Hargittai)

Development of PDBj-ML

The methodological advance of protein structure determination has resulted in accumulation of a large amount of structural information of biological macromolecules. With the advent of the structural genomics, the amount is expected to increase even more rapidly in the near future. Efficient methods of its storage and handling are, therefore, required to fully explore the information available. The Protein Data Bank (PDB) [1, 2] has been the primary database of the field, where atomic coordinates and other relevant information are stored in a well-known PDB format as well as the Macromolecular Crystallographic Information Format (mmCIF) [3]. Since last year Institute for Protein Science (IPR) of Osaka University has collected, curated and edited some of those coordinates as PDBjapan (PDBj) in collaboration with Research Collaboratory for Structural Bioinformatics (RCSB). In recent years, XML has been proven powerful in various fields for the exchange and archiving of data, including biology-oriented datebases. It would be useful, therefore, to have an XML version of PDB so that it can be easily combined with other biological databases. In an attempt to provide such an interface between structural data and other tools of bioinformatics, we have initiated development of PDBj-ML, an XML format for structural information of macromolecules.

Evolutionary Speed of Enzymes Functional Surfaces and their Relationship with Metabolic Fluxes in Networks of Central Carbon Metabolism of Bacteria

Understanding how enzymes emerge, interact, and work together in biochemical networks is a challenging task. The concentrations of enzymes, substrates, products, and their time-dependent changes, the organization of structures of enzymes involved, as well as their evolutionary histories should all be integrated for an improved comprehensive understanding of the systems behavior of the biochemical networks. In this study, we explore the relationship between enzyme structures, enzyme functions, metabolite concentrations and fluxes in the glycolysis and pentose-phosphate pathways, which form the central carbon metabolism of E. coli. As large amount of structural information is available for most components of these metabolic pathways, we have estimated the evolutionary rates of individual functional surface of enzymes in these pathways. To separate selection pressure due to enzyme function from other selection pressures such as structural integrity, folding stability, and folding kinetics, we have estimated the substitution rates of residues located in the functional pockets of these enzyme structures, including residues involved in the catalytic reaction, and residues in the surrounding area participating in the accommodation of both substrates and products. These are then summarized into an overall index of evolutionary speed for enzyme functional surfaces. In conjunction of stoichiometry based flux-balance analysis, we have also simulated time-dependent evolution of metabolite concentrations and fluxes based on ordinary differential equation models and currently available enzyme kinetic parameters. We discuss our findings in overall relationship of evolutionary speed of enzyme function and importance of individual enzyme measured by flux balance and kinetic flow analysis, in the global context of the central carbon metabolism of E coli.

Conformational Changes during Nucleotide Selection by Sulfolobus solfataricus DNA Polymerase Dpo4

The mechanism of nucleotide selection by Y-family DNA polymerases has been the subject of intense study, but significant structural contacts and/or conformational changes that relate to polymerase fidelity have been difficult to identify. Here we report on the conformational dynamics of a model Y-family polymerase Dpo4 from Sulfolobus solfataricus. Hydrogen-deuterium exchange in tandem with mass spectrometry was used to monitor changes in Dpo4 structure as a function of time and the presence or absence of specific substrates and ligands. Analysis of the data revealed previously unrecognized structural changes that accompany steps in the catalytic cycle leading up to phosphoryl transfer. For example, the solvent accessibility of the alphaB-loop-alphaC region in the finger domain decreased in the presence of all four dNTP insertion events, but the rate of deuterium exchange, an indicator of conformational flexibility, only decreased during an accurate insertion event. Of particular note is a change in the region surrounding the H-helix of the thumb domain. Upon binding DNA and Mg2+, the H-helix showed a decrease in solvent accessibility and flexibility that was relaxed only upon addition of dCTP, which forms a Watson-Crick base pair with template dG and not during mispairing events. The current study expands upon a previous report from our group that used a fluorescent probe located near the thumb domain to measure the kinetic properties of Dpo4 conformational changes. We now present a model for nucleotide selection by Dpo4 that arises from a synthesis of both structural and kinetic data.

Investigations of conformational transitions in proteins and RNA using 2DCOS Raman and 2DCOS Raman optical activity spectroscopies

Coupling the structural sensitivity of vibrational spectroscopies, such as Raman scattering and Raman optical activity (ROA), with the analytical insight provided by 2D generalized correlation analysis provides a new and exciting set of tools for structural biology. These new tools of 2DCos Raman and 2DCos ROA have the potential to provide new information on the mechanisms of conformational transitions of proteins and ribonucleic acids (RNA), such as those for folding and misfolding. However, the complexity of biomolecular transitions and the practicalities of spectroscopic data collection can make the analysis of 2DCos Raman and 2DCos ROA contour plots difficult. In this paper we present a summary of our methodology for obtaining reliable 2DCos contour maps and for their interpretation for folding and misfolding transitions in polypeptides, proteins and RNA. We demonstrate that our protocols for data pre-treatment greatly improve the quality of 2DCos contour maps, revealing the large amount of structural information that they contain, and then show that a moving window analysis is required to adequately follow biomolecular conformational transitions in detail.

Raman and Raman optical activity (ROA) analysis of RNA structural motifs

Raman and Raman optical activity (ROA) spectroscopies are well suited to the study of ribonucleic acids (RNA) as they are both sensitive to a large amount of structural information and applicable to most RNAs and experimental conditions. As such, they can fill the current gap between high- and low-resolution techniques currently used by RNA biologists. In this study we illustrate the complementarity of Raman and ROA spectroscopies by their application to two functional RNAs, a 37 nucleotide RNA fragment from Domain I of the encephalomyocarditis virus (EMCV) internal ribosome entry site (IRES) RNA and the L11 binding domain ribosomal RNA (LBD rRNA). Fingerprint spectra unique to the structure of each RNA are presented. We then discuss the Raman and ROA spectra of several oligonucleotides containing three common secondary structural motifs all found within the EMCV RNA; the GNRA tetraloop, pyrimidine-rich asymmetric bulge and a base mismatch. We show that highly detailed structural information can be obtained from these spectra.

Kinetic Investigation of Escherichia coli RNA Polymerase Mutants That Influence Nucleotide Discrimination and Transcription Fidelity

Recent RNA polymerase (RNAP) structures led to a proposed three-step model of nucleoside triphosphate (NTP) binding, discrimination, and incorporation. NTPs are thought to enter through the secondary channel, bind to an E site, rotate into a pre-insertion (PS) site, and ultimately align in the catalytic (A) site. We characterized the kinetics of correct and incorrect incorporation for several Escherichia coli RNAPs with substitutions in the proposed NTP entry pore (secondary channel). Substitutions of the semi-conserved residue betaAsp(675), which is >10A away from these sites, significantly reduce fidelity; however, substitutions of the totally conserved residues betaArg(678) and betaAsp(814) do not significantly alter the correct or incorrect incorporation kinetics, even though the corresponding residues in RNAPII crystal structures appear to be interacting with the NTP phosphate groups and coordinating the second magnesium ion in the active site, respectively. Structural analysis suggests that the lower fidelity of the betaAsp(675) mutants most likely results from reduction of the negative potential of a small pore between the E and PS sites and elimination of several structural interactions around the pore. We suggest a mechanism of nucleotide discrimination that is governed both by rotation of the NTP through this pore and subsequent rearrangement or closure of RNAP to align the NTP in the A site.

Molecular Modelling Approaches to the Design of Acetylcholinesterase Inhibitors: New Challenges for the Treatment of Alzheimers Disease

The interest for acetylcholinesterase as a target for the palliative treatment of Alzheimer's disease has been renewed in the last years owing to the evidences that support the role of this enzyme in accelerating the aggregation and deposition of the beta-amyloid peptide. A large amount of structural information on the acetylcholinesterase enzyme and of its complexes with inhibitors acting at the catalytic site, the peripheral binding site, or both is now available. Based on that, molecular modelling studies can be intensively used to decipher the molecular determinants that mediate the relationship between chemical structure and inhibitory potency. In turn, this knowledge can be exploited to design new compounds leading to more effective cholinergic strategies. At this point, inhibitors able to interact at the peripheral binding site are of particular relevance, as they might disrupt the interactions between the enzyme acetylcholinesterase and the beta-amyloid peptide. Therefore, these compounds might not only ameliorate the cholinergic deficit, but also be capable of slowing down the progression of the disease.

Structural basis of the matrix metalloproteinases and their physiological inhibitors, the tissue inhibitors of metalloproteinases.

The matrix metalloproteinases (MMPs) constitute a family of multidomain zinc endopeptidases with a metzincin-like catalytic domain, which are involved in extracellular matrix degradation but also in a number of other important biological processes. Under healthy conditions, their proteolytic activity is precisely regulated by their main endogenous protein inhibitors, the tissue inhibitors of metalloproteinases. Disruption of this balance results in pathophysiological processes such as arthritis, tumor growth and metastasis, rendering the MMPs attractive targets for inhibition therapy. Knowledge of their tertiary structures is crucial for a full understanding of their functional properties and for rational drug design. Since the first appearance of atomic MMP structures in 1994, a large amount of structural information has become available on the catalytic domains of MMPs and their substrate specificity, interaction with synthetic inhibitors and the TIMPs, the domain organization, and on complex formation with other proteins. This review will outline our current structural knowledge of the MMPs and the TIMPs.

Development of PDBj-ML

The methodological advance of protein structure determination has resulted in accumulation of a large amount of structural information of biological macromolecules. With the advent of the structural genomics, the amount is expected to increase even more rapidly in the near future. Efficient methods of its storage and handling are, therefore, required to fully explore the information available. The Protein Data Bank (PDB) [1, 2] has been the primary database of the field, where atomic coordinates and other relevant information are stored in a well-known PDB format as well as the Macromolecular Crystallographic Information Format (mmCIF) [3]. Since last year Institute for Protein Science (IPR) of Osaka University has collected, curated and edited some of those coordinates as PDBjapan (PDBj) in collaboration with Research Collaboratory for Structural Bioinformatics (RCSB). In recent years, XML has been proven powerful in various fields for the exchange and archiving of data, including biology-oriented datebases. It would be useful, therefore, to have an XML version of PDB so that it can be easily combined with other biological databases. In an attempt to provide such an interface between structural data and other tools of bioinformatics, we have initiated development of PDBj-ML, an XML format for structural information of macromolecules.

Rational Design of Reversible Acetylcholinesterase Inhibitors

A large amount of structural information on AChE and AChE-inhibitor complexes is currently available. Based on that, molecular modeling studies can be intensively used to gain insight into the mechanism of action of the enzyme and the molecular determinants that modulate the potency of inhibitors. In turn, this knowledge can be exploited to design new compounds leading to more effective cholinergic strategies. This manuscript reviews recent developments in the design of reversible acetylcholinesterase inhibitors.

Analysis of neutral isomeric low molecular weight carbohydrates using ferrocenyl boronate derivatization and tandem electrospray mass spectrometry.

A new analytical technique for small carbohydrates utilizing the cyclic ferrocenyl boronic esters (FcBors) of several neutral mono- and disaccharides is demonstrated. Distinction between the diastereomers of mono- and disaccharides is obtained. Analysis is by tandem electrospray-mass spectrometry (ES-MS) using a modified ion-source that promotes the preformation of ions. Selection of the molecular ion produced during single-electron oxidation of the ferrocene moiety of a specific population of saccharide isomers permits a variety of collisionally induced dissociation (CID) experiments. The resultant MS2/MS3 spectra reflect the ensemble of possible cyclic esters in equilibrium. An array of stable cross pyranose ring fragment ions representing sequential carbon loss as 30 u is observed. Consequently, the system provides an information-rich set of MSn spectra containing large amounts of structural information. Identification of D-glucose (D-Glc), its two commonly found epimers (D-mannose and D-galactose), and the two major L-diastereomers of 6-dideoxymonosaccharides, L-fucose (L-Fuc) and L-rhamnose (L-Rhm), are demonstrated. Selected pairs of disaccharides can be distinguished in terms of their anomeric linkage by reference to their MS3 spectra.