KING (Kinemage,Next Generation): A versatile interactive molecular and scientific visualization program

Proper visualization of scientific data is important for understanding spatial relationships. Particularly in the field of structural biology, where researchers seek to gain an understanding of the structure and function of biological macromolecules, it is important to have access to visualization programs which are fast, flexible, and customizable. We present KiNG, a Java program for visualizing scientific data, with a focus on macromolecular visualization. KiNG uses the kinemage graphics format, which is tuned for macromolecular structures, but is also ideal for many other kinds of spatially embedded information. KiNG is written in cross‐platform, open‐source Java code, and can be extended by end users through simple or elaborate “plug‐in” modules. Here, we present three such applications of KiNG to problems in structural biology (protein backbone rebuilding), bioinformatics of high‐dimensional data (e.g., protein sidechain chi angles), and classroom education (molecular illustration). KiNG is a mature platform for rapidly creating and capitalizing on scientific visualizations. As a research tool, it is invaluable as a test bed for new methods of visualizing scientific data and information. It is also a powerful presentation tool, whether for structure browsing, teaching, direct 3D display on the web, or as a method for creating pictures and videos for publications. KiNG is freely available for download at http://kinemage.biochem.duke.edu .     

Computational Methods Toward Unbiased Pattern Mining and Structure Determination in Cryo-Electron Tomography Data

Cryo-electron tomography can uniquely probe the native cellular environment for macromolecular structures. Tomograms feature complex data with densities of diverse, densely crowded macromolecular complexes, low signal-to-noise, and artifacts such as the missing wedge effect. Post-processing of this data generally involves isolating regions or particles of interest from tomograms, organizing them into related groups, and rendering final structures through subtomogram averaging. Template-matching and reference-based structure determination are popular analysis methods but are vulnerable to biases and can often require significant user input. Most importantly, these approaches cannot identify novel complexes that reside within the imaged cellular environment. To reliably extract and resolve structures of interest, efficient and unbiased approaches are therefore of great value. This review highlights notable computational software and discusses how they contribute to making automated structural pattern discovery a possibility. Perspectives emphasizing the importance of features for user-friendliness and accessibility are also presented.     

Mean curvature motion facilitates the segmentation and surface visualization of electron tomograms

Three-dimensional visualization of biological samples is essential for understanding their architecture and function. However, it is often challenging due to the macromolecular crowdedness of the samples and low signal-to-noise ratio of the cryo-electron tomograms. Denoising and segmentation techniques address this challenge by increasing the signal-to-noise ratio and by simplifying the data in images. Here, mean curvature motion is presented as a method that can be applied to segmentation results, created either manually or automatically, to significantly improve both the visual quality and downstream computational handling. Mean curvature motion is a process based on nonlinear anisotropic diffusion that smooths along edges and causes high-curvature features, such as noise, to disappear. In combination with level-set methods for image erosion and dilation, the application of mean curvature motion to electron tomograms and segmentations removes sharp edges or spikes in the visualized surfaces, produces an improved surface quality, and improves overall visualization and interpretation of the three-dimensional images.     

Visualizing biomolecular electrostatics in virtual reality with UnityMol‐APBS

Virtual reality is a powerful tool with the ability to immerse a user within a completely external environment. This immersion is particularly useful when visualizing and analyzing interactions between small organic molecules, molecular inorganic complexes, and biomolecular systems such as redox proteins and enzymes. A common tool used in the biomedical community to analyze such interactions is the Adaptive Poisson‐Boltzmann Solver (APBS) software, which was developed to solve the equations of continuum electrostatics for large biomolecular assemblages. Numerous applications exist for using APBS in the biomedical community including analysis of protein ligand interactions and APBS has enjoyed widespread adoption throughout the biomedical community. Currently, typical use of the full APBS toolset is completed via the command line followed by visualization using a variety of two‐dimensional external molecular visualization software. This process has inherent limitations: visualization of three‐dimensional objects using a two‐dimensional interface masks important information within the depth component. Herein, we have developed a single application, UnityMol‐APBS, that provides a dual experience where users can utilize the full range of the APBS toolset, without the use of a command line interface, by use of a simple graphical user interface (GUI) for either a standard desktop or immersive virtual reality experience.     

GPU-powered tools boost molecular visualization

Recent advances in experimental structure determination provide a wealth of structural data on huge macromolecular assemblies such as the ribosome or viral capsids, available in public databases. Further structural models arise from reconstructions using symmetry orders or fitting crystal structures into low-resolution maps obtained by electron-microscopy or small angle X-ray scattering experiments. Visual inspection of these huge structures remains an important way of unravelling some of their secrets. However, such visualization cannot conveniently be carried out using conventional rendering approaches, either due to performance limitations or due to lack of realism. Recent developments, in particular drawing benefit from the capabilities of Graphics Processing Units (GPUs), herald the next generation of molecular visualization solutions addressing these issues. In this article, we present advances in computer science and visualization that help biologists visualize, understand and manipulate large and complex molecular systems, introducing concepts that remain little-known in the bioinformatics field. Furthermore, we compile currently available software and methods enhancing the shape perception of such macromolecular assemblies, for example based on surface simplification or lighting ameliorations.     

VIRS: A visual tool for identifying restriction sites in multiple DNA sequences

VIRS (A visual tool for identifying restriction sites in multiple DNA sequences) is an interactive web‐based program designed for restriction endonuclease cut sites prediction and visualization. It can afford to analyze multiple DNA sequences simultaneously and produce visual restriction maps with several useful options intended for users' customization. These options also perform in‐depth analysis of the restriction maps, such as providing virtual electrophoretic result for digested fragments. Different from other analytical tools, VIRS not only displays visual outputs but also provides the detailed properties of restriction endonucleases that are commercially available. All the information of these enzymes is stored in our internal database, which is updated monthly from the manufacturers' web pages. It is freely available online at http://bis.zju.edu.cn/virs/index.html . © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009     

Detection of Macromolecular Fractions in HCN Polymers Using Electrophoretic and Ultrafiltration Techniques

Elucidating the origin of life involves synthetic as well as analytical challenges. Herein, for the first time, we describe the use of gel electrophoresis and ultrafiltration to fractionate HCN polymers. Since the first prebiotic synthesis of adenine by Oró, HCN polymers have gained much interest in studies on the origins of life due to the identification of biomonomers and related compounds within them. Here, we demonstrate that macromolecular fractions with electrophoretic mobility can also be detected within HCN polymers. The migration of polymers under the influence of an electric field depends not only on their sizes (one‐dimensional electrophoresis) but also their different isoelectric points (two‐dimensional electrophoresis, 2‐DE). The same behaviour was observed for several macromolecular fractions detected in HCN polymers. Macromolecular fractions with apparent molecular weights as high as 250 kDa were detected by tricine‐SDS gel electrophoresis. Cationic macromolecular fractions with apparent molecular weights as high as 140 kDa were also detected by 2‐DE. The HCN polymers synthesized were fractionated by ultrafiltration. As a result, the molecular weight distributions of the macromolecular fractions detected in the HCN polymers directly depended on the synthetic conditions used to produce these polymers. The implications of these results for prebiotic chemistry will be discussed.     

DNABind: A hybrid algorithm for structure‐based prediction of DNA‐binding residues by combining machine learning‐ and template‐based approaches

Accurate prediction of DNA‐binding residues has become a problem of increasing importance in structural bioinformatics. Here, we presented DNABind, a novel hybrid algorithm for identifying these crucial residues by exploiting the complementarity between machine learning‐ and template‐based methods. Our machine learning‐based method was based on the probabilistic combination of a structure‐based and a sequence‐based predictor, both of which were implemented using support vector machines algorithms. The former included our well‐designed structural features, such as solvent accessibility, local geometry, topological features, and relative positions, which can effectively quantify the difference between DNA‐binding and nonbinding residues. The latter combined evolutionary conservation features with three other sequence attributes. Our template‐based method depended on structural alignment and utilized the template structure from known protein–DNA complexes to infer DNA‐binding residues. We showed that the template method had excellent performance when reliable templates were found for the query proteins but tended to be strongly influenced by the template quality as well as the conformational changes upon DNA binding. In contrast, the machine learning approach yielded better performance when high‐quality templates were not available (about 1/3 cases in our dataset) or the query protein was subject to intensive transformation changes upon DNA binding. Our extensive experiments indicated that the hybrid approach can distinctly improve the performance of the individual methods for both bound and unbound structures. DNABind also significantly outperformed the state‐of‐art algorithms by around 10% in terms of Matthews's correlation coefficient. The proposed methodology could also have wide application in various protein functional site annotations. DNABind is freely available at http://mleg.cse.sc.edu/DNABind/ . Proteins 2013; 81:1885–1899. © 2013 Wiley Periodicals, Inc.     

SIMLR: A Tool for Large‐Scale Genomic Analyses by Multi‐Kernel Learning

SIMLR (S ingle‐cell I nterpretation via M ulti‐kernel L eaR ning), an open‐source tool that implements a novel framework to learn a sample‐to‐sample similarity measure from expression data observed for heterogenous samples, is presented here. SIMLR can be effectively used to perform tasks such as dimension reduction, clustering, and visualization of heterogeneous populations of samples. SIMLR was benchmarked against state‐of‐the‐art methods for these three tasks on several public datasets, showing it to be scalable and capable of greatly improving clustering performance, as well as providing valuable insights by making the data more interpretable via better a visualization. SIMLR is available on https://github.com/BatzoglouLabSU/SIMLR GitHub in both R and MATLAB implementations. Furthermore, it is also available as an R package on http://bioconductor.org     

Comprehensive modeling and functional analysis of Toll‐like receptor ligand‐recognition domains

Toll‐like receptors (TLRs) are innate immune pattern‐recognition receptors endowed with the capacity to detect microbial pathogens based on pathogen‐associated molecular patterns. The understanding of the molecular principles of ligand recognition by TLRs has been greatly accelerated by recent structural information, in particular the crystal structures of leucine‐rich repeat‐containing ectodomains of TLR2, 3, and 4 in complex with their cognate ligands. Unfortunately, for other family members such as TLR7, 8, and 9, no experimental structural information is currently available. Methods such as X‐ray crystallography or nuclear magnetic resonance are not applicable to all proteins. Homology modeling in combination with molecular dynamics may provide a straightforward yet powerful alternative to obtain structural information in the absence of experimental (structural) data, provided that the generated three‐dimensional models adequately approximate what is found in nature. Here, we report the development of modeling procedures tailored to the structural analysis of the extracellular domains of TLRs. We comprehensively compared secondary structure, torsion angles, accessibility for glycosylation, surface charge, and solvent accessibility between published crystal structures and independently built TLR2, 3, and 4 homology models. Finding that models and crystal structures were in good agreement, we extended our modeling approach to the remaining members of the TLR family from human and mouse, including TLR7, 8, and 9.     

Emergent Pseudocapacitance of 2D Nanomaterials

Two dimensional (2D) nanomaterials are very attractive due to their unique structural and surface features for energy storage applications. Motivated by the recent pioneering works demonstrating “the emergent pseudocapacitance of 2D nanomaterials,” the energy storage and nanoscience communities could revisit bulk layered materials though state‐of‐the‐art nanotechnology such as nanostructuring, nanoarchitecturing, and compositional control. However, no review has focused on the fundamentals, recent progress, and outlook on this new mechanism of 2D nanomaterials yet. In this study, the key aspects of emergent pseudocapacitors based on 2D nanomaterials are comprehensively reviewed, which covers the history, classification, thermodynamic and kinetic aspects, electrochemical characteristics, and design guidelines of materials for extrinsically surface redox and intercalation pseudocapacitors. The structural and compositional controls of graphene and other carbon nanosheets, transition metal oxides and hydroxides, transition metal dichalcogenides, and metal carbide/nitride on both microscopic and macroscopic levels will be particularly addressed, emphasizing the important results published since 2010. Finally, perspectives on the current impediments and future directions of this field are offered. Unlimited combinations and modifications of 2D nanomaterials can provide a rational strategy to overcome intrinsic limitations of existing materials, offering a new‐generation energy storage materials toward a high and new position in the Ragone plot.     

Label‐free optical projection tomography for quantitative three‐dimensional anatomy of mouse embryo

Recent progress in three‐dimensional optical imaging techniques allows visualization of many comprehensive biological specimens. Optical clearing methods provide volumetric and quantitative information by overcoming the limited depth of light due to scattering. However, current imaging technologies mostly rely on the synthetic or genetic fluorescent labels, thus limits its application to whole‐body visualization of generic mouse models. Here, we report a label‐free optical projection tomography (LF‐OPT) technique for quantitative whole mouse embryo imaging. LF‐OPT is based on the attenuation contrast of light rather than fluorescence, and it utilizes projection imaging technique similar to computed tomography for visualizing the volumetric structure. We demonstrate this with a collection of mouse embryo morphologies in different stages using LF‐OPT. Additionally, we extract quantitative organ information applicable toward high‐throughput phenotype screening. Our results indicate that LF‐OPT can provide multi‐scale morphological information in various tissues including bone, which can be difficult in conventional optical imaging technique.     

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Modern 3D electron microscopy approaches have recently allowed unprecedented insight into the 3D ultrastructural organization of cells and tissues, enabling the visualization of large macromolecular machines, such as adhesion complexes, as well as higher-order structures, such as the cytoskeleton and cellular organelles in their respective cell and tissue context. Given the inherent complexity of cellular volumes, it is essential to first extract the features of interest in order to allow visualization, quantification, and therefore comprehension of their 3D organization. Each data set is defined by distinct characteristics, e.g., signal-to-noise ratio, crispness (sharpness) of the data, heterogeneity of its features, crowdedness of features, presence or absence of characteristic shapes that allow for easy identification, and the percentage of the entire volume that a specific region of interest occupies. All these characteristics need to be considered when deciding on which approach to take for segmentation.The six different 3D ultrastructural data sets presented were obtained by three different imaging approaches: resin embedded stained electron tomography, focused ion beam- and serial block face- scanning electron microscopy (FIB-SEM, SBF-SEM) of mildly stained and heavily stained samples, respectively. For these data sets, four different segmentation approaches have been applied: (1) fully manual model building followed solely by visualization of the model, (2) manual tracing segmentation of the data followed by surface rendering, (3) semi-automated approaches followed by surface rendering, or (4) automated custom-designed segmentation algorithms followed by surface rendering and quantitative analysis. Depending on the combination of data set characteristics, it was found that typically one of these four categorical approaches outperforms the others, but depending on the exact sequence of criteria, more than one approach may be successful. Based on these data, we propose a triage scheme that categorizes both objective data set characteristics and subjective personal criteria for the analysis of the different data sets.     

A pore‐hindered diffusion and reaction model can help explain the importance of pore size distribution in enzymatic hydrolysis of biomass

Until now, most efforts to improve monosaccharide production from biomass through pretreatment and enzymatic hydrolysis have used empirical optimization rather than employing a rational design process guided by a theory‐based modeling framework. For such an approach to be successful a modeling framework that captures the key mechanisms governing the relationship between pretreatment and enzymatic hydrolysis must be developed. In this study, we propose a pore‐hindered diffusion and kinetic model for enzymatic hydrolysis of biomass. When compared to data available in the literature, this model accurately predicts the well‐known dependence of initial cellulose hydrolysis rates on surface area available to a cellulase‐size molecule. Modeling results suggest that, for particles smaller than 5 × 10^?3 cm, a key rate‐limiting step is the exposure of previously unexposed cellulose occurring after cellulose on the surface has hydrolyzed, rather than binding or diffusion. However, for larger particles, according to the model, diffusion plays a more significant role. Therefore, the proposed model can be used to design experiments that produce results that are either affected or unaffected by diffusion. Finally, by using pore size distribution data to predict the biomass fraction that is accessible to degradation, this model can be used to predict cellulose hydrolysis with time using only pore size distribution and initial composition data. Biotechnol. Bioeng. 2013; 110: 127–136. © 2012 Wiley Periodicals, Inc.     

Radial organization of interstitial exchange pathway and influence of collagen in synovium.

The synovial intercellular space is the path by which water, nutrients, cytokines, and macromolecules enter and leave the joint cavity. In this study two structural factors influencing synovial permeability were quantified by morphometry (Delesse's principle) of synovial electronmicrographs (rabbit knee), namely interstitial volume fraction Vv.1 and the fraction of the interstitium obstructed by collagen fibrils. Mean Vv.1 across the full thickness was 0.66 +/- 0.03 SEM (n = 11); but Vv.1 actually varied systematically with depth normal to the surface, increasing nonlinearly from 0.40 +/- 0.04 (n = 5 joints) near the free surface to 0.92 +/- 0.02 near the subsynovial interface. Tending to offset this increase in transport space, however, the space "blocked" by collagen fibrils also increased nonlinearly with depth. Bundles of collagen fibrils occupied 13.6 +/- 2.4% of interstitial volume close to the free surface but 49 +/- 4.8% near the subsynovial surface (full-thickness average, 40.5 +/- 3.5%), with fibrils accounting for 48.6-57.1% of the bundle space. Because of the two counteracting compositional gradients, the space available for fibril-excluded transport (hydraulic flow and macromolecular diffusion) was relatively constant > 4 microns below the surface but constricted at the synovium-cavity interface. The space available to extracellular polymers was only 51-53% of tissue volume, raising their effective concentration and hence the lining's resistance to flow and ability to confine the synovial fluid.     

A highly sensitive and temporal visualization system for gaseous ethanol with chemiluminescence enhancer

A two‐dimensional gaseous ethanol visualization system has been developed and demonstrated using a horseradish peroxidase–luminol–hydrogen peroxide system with high‐purity luminol solution and a chemiluminescence (CL) enhancer. This system measures ethanol concentrations as intensities of CL via the luminol reaction. CL was emitted when the gaseous ethanol was injected onto an enzyme‐immobilized membrane, which was employed as a screen for two‐dimensional gas visualization. The average intensity of CL on the substrate was linearly related to the concentration of standard ethanol gas. These results were compared with the CL intensity of the CCD camera recording image in the visualization system. This system is available for gas components not only for spatial but also for temporal analysis in real time. A high‐purity sodium salt HG solution (L‐HG) instead of standard luminol solution and an enhancer, eosin Y (EY) solution, were adapted for improvement of CL intensity of the system. The visualization of gaseous ethanol was achieved at a detection limit of 3 ppm at optimized concentrations of L‐HG solution and EY. Copyright © 2011 John Wiley & Sons, Ltd.     

A New Entodiniomorphid Ciliate,Troglocorys cava n. g., n. sp., from the Wild Eastern Chimpanzee (Pan troglodytes schweinfurthii) from Uganda

ABSTRACT. Troglocorys cava n. g., n. sp. is described from the feces of wild eastern chimpanzee, Pan troglodytes schweinfurthii, in Uganda. This new species has a spherical body with a frontal lobe, a long vestibulum, a cytoproct located at the posterior dorsal side of the body, an ovoid macronucleus, a contractile vacuole near the cytoproct, and a large concavity on the left surface of the body. Buccal ciliature is non‐retractable and consists of three ciliary zones: an adoral zone surrounding the vestibular opening, a dorso‐adoral zone extending transversely at the basis of the frontal lobe, and a vestibular zone longitudinally extending in a gently spiral curve to line the surface of the vestibulum. Two non‐retractable somatic ciliary zones comprise arches over the body surface: a short dorsal ciliary arch extending transversely at the basis of the frontal lobe and a wide C‐shaped left ciliary arch in the left concavity. Because of the presence of three ciliary zones in the non‐retractable buccal ciliature, the present genus might be a member of the family Blepharocorythidae, but the large left concavity and the C‐shaped left ciliary arch are unique, such structures have never been described from other blepharocorythids.     

GRASS: a server for the graphical representation and analysis of structures

GRASS (Graphical Representation and Analysis of Structures Server), a new web-based server, is described. GRASS exploits many of the features of the GRASP program and is designed to provide interactive molecular graphics and quantitative analysis tools with a simple interface over the World-Wide Web. Using GRASS, it is now possible to view many surface features of biological macromolecules on either standard workstations used in macromolecular analysis or personal computers. The result is a World-Wide Web-based, platform-independent, easily used tool for macromolecular visualization and structure analysis.     

Vertical niche partitioning between cryptic sibling species of a cosmopolitan marine planktonic protist

A large portion of the surface‐ocean biomass is represented by microscopic unicellular plankton. These organisms are functionally and morphologically diverse, but it remains unclear how their diversity is generated. Species of marine microplankton are widely distributed because of passive transport and lack of barriers in the ocean. How does speciation occur in a system with a seemingly unlimited dispersal potential? Recent studies using planktonic foraminifera as a model showed that even among the cryptic genetic diversity within morphological species, many genetic types are cosmopolitan, lending limited support for speciation by geographical isolation. Here we show that the current two‐dimensional view on the biogeography and potential speciation mechanisms in the microplankton may be misleading. By depth‐stratified sampling, we present evidence that sibling genetic types in a cosmopolitan species of marine microplankton, the planktonic foraminifer Hastigerina pelagica, are consistently separated by depth throughout their global range. Such strong separation between genetically closely related and morphologically inseparable genetic types indicates that niche partitioning in marine heterotrophic microplankton can be maintained in the vertical dimension on a global scale. These observations indicate that speciation along depth (depth‐parapatric speciation) can occur in vertically structured microplankton populations, facilitating diversification without the need for spatial isolation.