BACKGROUND: Protein structures and their interaction with ligands have been in the focus of biochemistry and structural biology research for decades. The transportation of ligand into the protein active site is often complex process, driven by geometric and physico-chemical properties, which renders the ligand path full of jitter and impasses. This prevents understanding of the ligand transportation and reasoning behind its behavior along the path. RESULTS: To address the needs of the domain experts we design an explorative visualization solution based on a multi-scale simplification model. It helps to navigate the user to the most interesting parts of the ligand trajectory by exploring different attributes of the ligand and its movement, such as its distance to the active site, changes of amino acids lining the ligand, or ligand "stuckness". The process is supported by three linked views - 3D representation of the simplified trajectory, scatterplot matrix, and bar charts with line representation of ligand-lining amino acids. CONCLUSIONS: The usage of our tool is demonstrated on molecular dynamics simulations provided by the domain experts. The tool was tested by the domain experts from protein engineering and the results confirm that it helps to navigate the user to the most interesting parts of the ligand trajectory and to understand the ligand behavior.

BACKGROUND: Protein function is determined by many factors, namely by its constitution, spatial arrangement, and dynamic behavior. Studying these factors helps the biochemists and biologists to better understand the protein behavior and to design proteins with modified properties. One of the most common approaches to these studies is to compare the protein structure with other molecules and to reveal similarities and differences in their polypeptide chains. RESULTS: We support the comparison process by proposing a new visualization technique that bridges the gap between traditionally used 1D and 3D representations. By introducing the information about mutual positions of protein chains into the 1D sequential representation the users are able to observe the spatial differences between the proteins without any occlusion commonly present in 3D view. Our representation is designed to serve namely for comparison of multiple proteins or a set of time steps of molecular dynamics simulation. CONCLUSIONS: The novel representation is demonstrated on two usage scenarios. The first scenario aims to compare a set of proteins from the family of cytochromes P450 where the position of the secondary structures has a significant impact on the substrate channeling. The second scenario focuses on the protein flexibility when by comparing a set of time steps our representation helps to reveal the most dynamically changing parts of the protein chain.

Abstract Structural properties of molecules are of primary concern in many fields. This report provides a comprehensive overview on techniques that have been developed in the fields of molecular graphics and visualization with a focus on applications in structural biology. The field heavily relies on computerized geometric and visual representations of three‐dimensional, complex, large and time‐varying molecular structures. The report presents a taxonomy that demonstrates which areas of molecular visualization have already been extensively investigated and where the field is currently heading. It discusses visualizations for molecular structures, strategies for efficient display regarding image quality and frame rate, covers different aspects of level of detail and reviews visualizations illustrating the dynamic aspects of molecular simulation data. The survey concludes with an outlook on promising and important research topics to foster further success in the development of tools that help to reveal molecular secrets.

The reactivity of the biomolecular structures is highly influenced by their structural features. Thus, studying these features along with the exploration of their dynamic behavior helps to understand the processes ongoing in living cells. This can be reached by the visual representation of these processes as visualization is one of the most natural ways to convey such information. However, none of the currently available techniques provides the biochemists with an intuitive real-time representation of the dynamic movements of molecules and precise geometrical based extraction of their structural features performed instantly. In this paper we introduce such a technique enabling the user to compute and also to visualize the molecular surface along with inner voids. To obtain a better insight into the molecule, our technique enables to visualize the molecular surface transparently. The opacity can be adjusted by changing user-defined parameters in order to enhance the perception of the surfaces of inner voids. All integrated algorithms run in real-time which gives the user a big variety of exploration possibilities. The importance of our approach is even amplified with respect to the fact that currently the size of molecular dynamics simulations is increasing dramatically and offline rendering thus becomes impracticable. The usability of our technique was evaluated by the domain experts.

Abstract In this report we review and structure the branch of molecular visualization that is concerned with the visual analysis of cavities in macromolecular protein structures. First the necessary background, the domain terminology, and the goals of analytical reasoning are introduced. Based on a comprehensive collection of relevant research works, we present a novel classification for cavity detection approaches and structure them into four distinct classes: grid‐based, Voronoi‐based, surface‐based, and probe‐based methods. The subclasses are then formed by their combinations. We match these approaches with corresponding visualization technologies starting with direct 3D visualization, followed with non‐spatial visualization techniques that for example abstract the interactions between structures into a relational graph, straighten the cavity of interest to see its profile in one view, or aggregate the time sequence into a single contour plot. We also discuss the current state of methods for the visual analysis of cavities in dynamic data such as molecular dynamics simulations. Finally, we give an overview of the most common tools that are actively developed and used in the structural biology and biochemistry research. Our report is concluded by an outlook on future challenges in the field.

Animated movies are a popular way to communicate complex phenomena in cell biology to the broad audience. Animation artists apply sophisticated illustration techniques to communicate a story, while trying to maintain a realistic representation of a complex dynamic environment. Since such hand-crafted animations are time-consuming and cost-intensive to create, our goal is to formalize illustration techniques used by artists to facilitate the automatic creation of visualizations generated from mesoscale particle-based molecular simulations. Our technique Illustrative Timelapse supports visual exploration of complex biochemical processes in dynamic environments by (1) seamless temporal zooming to observe phenomena in different temporal resolutions, (2) visual abstraction of molecular trajectories to ensure that observers are able to visually follow the main actors, (3) increased visual focus on events of interest, and (4) lens effects to preserve a realistic representation of the environment in the context. Results from a first user study indicate that visual abstraction of trajectories improves the ability to follow a story and is also appreciated by users. Lens effects increased the perceived amount of molecular motion in the environment while trading off traceability of individual molecules.

BACKGROUND: Research in cell biology is steadily contributing new knowledge about many aspects of physiological processes, both with respect to the involved molecular structures as well as their related function. Illustrations of the spatio-temporal development of such processes are not only used in biomedical education, but also can serve scientists as an additional platform for in-silico experiments. RESULTS: In this paper, we contribute a new, three-level modeling approach to illustrate physiological processes from the class of polymerization at different time scales. We integrate physical and empirical modeling, according to which approach best suits the different involved levels of detail, and we additionally enable a form of interactive steering, while the process is illustrated. We demonstrate the suitability of our approach in the context of several polymerization processes and report from a first evaluation with domain experts. CONCLUSION: We conclude that our approach provides a new, hybrid modeling approach for illustrating the process of emergence in physiology, embedded in a densely filled environment. Our approach of a complementary fusion of three systems combines the strong points from the different modeling approaches and is capable to bridge different spatial and temporal scales.

Abstract In this paper we propose a new type of a particle systems, tailored for illustrative visualization purposes, in particular for visualizing molecular reactions in biological networks. Previous visualizations of biochemical processes were exploiting the results of agent‐based modeling. Such modeling aims at reproducing accurately the stochastic nature of molecular interactions. However, it is impossible to expect events of interest happening at a certain time and location, which is impractical for storytelling. To obtain the means of controlling molecular interactions, we propose to govern passive agents with an omniscient intelligence, instead of giving to the agents the freedom of initiating reaction autonomously. This makes it possible to generate illustrative animated stories that communicate the functioning of the molecular machinery. The rendering performance delivers for interactive framerates of massive amounts of data, based on the dynamic tessellation capabilities of modern graphics cards. Finally, we report an informal expert feedback we obtained from the potential users.

Abstract Molecular visualization is often challenged with rendering of large molecular structures in real time. We introduce a novel approach that enables us to show even large protein complexes. Our method is based on the level‐of‐detail concept, where we exploit three different abstractions combined in one visualization. Firstly, molecular surface abstraction exploits three different surfaces, solvent‐excluded surface (SES), Gaussian kernels and van der Waals spheres, combined as one surface by linear interpolation. Secondly, we introduce three shading abstraction levels and a method for creating seamless transitions between these representations. The SES representation with full shading and added contours stands in focus while on the other side a sphere representation of a cluster of atoms with constant shading and without contours provide the context. Thirdly, we propose a hierarchical abstraction based on a set of clusters formed on molecular atoms. All three abstraction models are driven by one importance function classifying the scene into the near‐, mid‐ and far‐field. Moreover, we introduce a methodology to render the entire molecule directly using the A‐buffer technique, which further improves the performance. The rendering performance is evaluated on series of molecules of varying atom counts.

Molecular surfaces provide a useful mean for analyzing interactions between biomolecules; such as identification and characterization of ligand binding sites to a host macromolecule. We present a novel technique, which extracts potential binding sites, represented by cavities, and characterize them by 3D graphs and by amino acids. The binding sites are extracted using an implicit function sampling and graph algorithms. We propose an advanced cavity exploration technique based on the graph parameters and associated amino acids. Additionally, we interactively visualize the graphs in the context of the molecular surface. We apply our method to the analysis of MD simulations of Proteinase 3, where we verify the previously described cavities and suggest a new potential cavity to be studied.

Representation of molecular surfaces is a well established way to study the interaction of molecules. The state-of-the-art molecular representation is the SES model, which provides a detailed surface visualization. Nevertheless, it is computationally expensive, so the less accurate Gaussian model is traditionally preferred. We introduce a novel surface representation that resembles the SES and approaches the rendering performance of the Gaussian model. Our technique is based on the iterative blending of implicit functions and avoids any pre-computation. Additionally, we propose a GPU-based ray-casting algorithm that efficiently visualize our molecular representation. A qualitative and quantitative comparison of our model with respect to the Gaussian and SES models is presented. As showcased in the paper, our technique is a valid and appealing alternative to the Gaussian representation. This is especially relevant in all the applications where the cost of the SES is prohibitive.

Molecular surfaces are an established tool to analyze and to study the evolution and interaction of molecules. One of the most advanced representations of molecular surfaces is called the solvent excluded surface. We present a novel and a simple method for representing the solvent excluded surfaces (SES). Our method requires no precomputation and therefore allows us to vary SES parameters outright. We utilize the theory of implicit surfaces and their CSG operations to compose the implicit function representing the molecular surface locally. This function returns a minimal distance to the SES representation. Additionally, negative values of the implicit function determine that the point lies outside SES whereas positive ones that the point lies inside. We describe how to build this implicit function composed of three types of patches constituting the SES representation. Finally, we propose a method to visualize the iso-surface of the implicit function by means of ray-casting and the set of rendering parameters affecting the overall performance.

Abstract Cluster analysis is a useful method which reveals underlying structures and relations of items after grouping them into clusters. In the case of temporal data, clusters are defined over time intervals where they usually exhibit structural changes. Conventional cluster analysis does not provide sufficient methods to analyze these structural changes, which are, however, crucial in the interpretation and evaluation of temporal clusters. In this paper, we present two novel and interactive visualization techniques that enable users to explore and interpret the structural changes of temporal clusters. We introduce the temporal cluster view, which visualizes the structural quality of a number of temporal clusters, and temporal signatures, which represents the structure of clusters over time. We discuss how these views are utilized to understand the temporal evolution of clusters. We evaluate the proposed techniques in the cluster analysis of mixed lipid bilayers.