Scale-Space Splatting: Reforming Spacetime for the Cross-Scale Exploration of Integral Measures in Molecular Dynamics

Understanding large amounts of spatiotemporal data from particle-based simulations, such as molecular dynamics, often relies on the computation and analysis of aggregate measures. These, however, by virtue of aggregation, hide structural information about the space/time localization of the studied phenomena. This leads to degenerate cases where the measures fail to capture distinct behaviour. In order to drill into these aggregate values, we propose a multi-scale visual exploration technique. Our novel representation, based on partial domain aggregation, enables the construction of a continuous scale-space for discrete datasets and the simultaneous exploration of scales in both space and time. We link these two scale-spaces in a scale-space space-time cube and model linked views as orthogonal slices through this cube, thus enabling the rapid identification of spatio-temporal patterns at multiple scales. To demonstrate the effectiveness of our approach, we showcase an advanced exploration of a protein-ligand simulation.Comment: 11 pages, 9 figures, IEEE SciVis 201

sMolBoxes: Dataflow Model for Molecular Dynamics Exploration

We present sMolBoxes, a dataflow representation for the exploration and analysis of long molecular dynamics (MD) simulations. When MD simulations reach millions of snapshots, a frame-by-frame observation is not feasible anymore. Thus, biochemists rely to a large extent only on quantitative analysis of geometric and physico-chemical properties. However, the usage of abstract methods to study inherently spatial data hinders the exploration and poses a considerable workload. sMolBoxes link quantitative analysis of a user-defined set of properties with interactive 3D visualizations. They enable visual explanations of molecular behaviors, which lead to an efficient discovery of biochemically significant parts of the MD simulation. sMolBoxes follow a node-based model for flexible definition, combination, and immediate evaluation of properties to be investigated. Progressive analytics enable fluid switching between multiple properties, which facilitates hypothesis generation. Each sMolBox provides quick insight to an observed property or function, available in more detail in the bigBox View. The case study illustrates that even with relatively few sMolBoxes, it is possible to express complex analyses tasks, and their use in exploratory analysis is perceived as more efficient than traditional scripting-based methods.Comment: 10 pages, 9 figures, IEEE VIS, TVC

Additional file 3 of COZOID: contact zone identifier for visual analysis of protein-protein interactions

Example data. Testing dataset used in the manuscript. (ZIP 2662 kb

Additional file 2 of COZOID: contact zone identifier for visual analysis of protein-protein interactions

Software build. Executable binary file of the software tool. (ZIP 118,784 kb

Additional file 4 of COZOID: contact zone identifier for visual analysis of protein-protein interactions

User guide. User guide for the software tool. (PDF 3502 kb

State of the Art of Molecular Visualization in Immersive Virtual Environments

International audienceVisualization plays a crucial role in molecular and structural biology. It has been successfully applied to a variety of tasks, including structural analysis and interactive drug design. While some of the challenges in this area can be overcome with more advanced visualization and interaction techniques, others are challenging primarily due to the limitations of the hardware devices used to interact with the visualized content. Consequently, visualization researchers are increasingly trying to take advantage of new technologies to facilitate the work of domain scientists. Some typical problems associated with classic 2D interfaces, such as regular desktop computers, are a lack of natural spatial understanding and interaction, and a limited field of view. These problems could be solved by immersive virtual environments and corresponding hardware, such as virtual reality head-mounted displays. Thus, researchers are investigating the potential of immersive virtual environments in the field of molecular visualization. There is already a body of work ranging from educational approaches to protein visualization to applications for collaborative drug design. This review focuses on molecular visualization in immersive virtual environments as a whole, aiming to cover this area comprehensively. We divide the existing papers into different groups based on their application areas, and types of tasks performed. Further, we also include a list of available software tools. We conclude the report with a discussion of potential future research on molecular visualization in immersive environments

CAVER Analyst 2.0: Analýza a vizualizace kanálů a tunelů v proteinových strukturách a trajektoriích molekulární dynamiky

Motivace: Studování transportních cest ligandů, molekul rozpouštědla nebo iontů v trans-membránových proteinech a proteinech s hluboce skrytými aktivním místy tvoří základ k porozumění jejich biologické funkce. Detailní analýza strukturálních vlastností ovlivňujících transportní cesty je také důležitá při výrobě proteinů pro bio-medicínské a bio-technologické aplikace. Výsledky: CAVER Analyst 2.0 je softwarový nástroj pro kvantitativní analýzu a vizualizaci tunelů a kanálů v reálném čase ve statických a dynamických strukturách. Tato verze poskytuje uživatelům mnoho nových funkcí zahrnujících pokročilé techniky intuitivní vizuální inspekce prostoro-časového chování tunelů a kanálů. Novátorské algoritmy zde přináší možnost provádět efektivní analýzu a redukci dat ve velkých proteinových strukturách a simulacích molekulární dynamiky. Dostupnost a implementace: CAVER Analyst 2.0 je multi-platformní samostatná aplikace založená na jazyce Java. Aplikace a dokumentace jsou volně k dispozici na adrese www.caver.cz.Motivation: Studying the transport paths of ligands, solvents, or ions in transmembrane proteins and proteins with buried binding sites is fundamental to the understanding of their biological function. A detailed analysis of the structural features influencing the transport paths is also important for engineering proteins for biomedical and biotechnological applications. Results: CAVER Analyst 2.0 is a software tool for quantitative analysis and real-time visualization of tunnels and channels in static and dynamic structures. This version provides the users with many new functions, including advanced techniques for intuitive visual inspection of the spatiotemporal behavior of tunnels and channels. Novel integrated algorithms allow an efficient analysis and data reduction in large protein structures and molecular dynamic simulations. Availability and implementation: CAVER Analyst 2.0 is a multi-platform standalone Java-based application. Binaries and documentation are freely available at www.caver.cz