Seeing the unseen: Comparison study of representation approaches for biochemical processes in education.

The representations of biochemical processes must balance visual portrayals with descriptive content to be an effective learning tool. To determine what type of representation is the most suitable for education, we designed five different representations of adenosine triphosphate (ATP) synthesis and examined how they are perceived. Our representations consisted of an overview of the process in a detailed and abstract illustrative format, continuous video formats with and without narration, and a combined illustrative overview with dynamic components. The five representations were evaluated by non-experts who were randomly assigned one of them and experts who viewed and compared all five representations. Subsequently, we conducted a focus group on the outcomes of these evaluations, which gave insight into possible explanations of our results, where the non-experts preferred the detailed static representation and found the narrated video least helpful, in contradiction to the experts who favored the narrated video the most.

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 studies illustrate that even with relatively few sMolBoxes, it is possible to express complex analytical tasks, and their use in exploratory analysis is perceived as more efficient than traditional scripting-based methods.

LoopGrafter: Visual Support for the Grafting Workflow of Protein Loops.

In the process of understanding and redesigning the function of proteins in modern biochemistry, protein engineers are increasingly focusing on the exploration of regions in proteins called loops. Analyzing various characteristics of these regions helps the experts to design the transfer of the desired function from one protein to another. This process is denoted as loop grafting. As this process requires extensive manual treatment and currently there is no proper visual support for it, we designed LoopGrafter: a web-based tool that provides experts with visual support through all the loop grafting pipeline steps. The tool is logically divided into several phases, starting with the definition of two input proteins and ending with a set of grafted proteins. Each phase is supported by a specific set of abstracted 2D visual representations of loaded proteins and their loops that are interactively linked with the 3D view onto proteins. By sequentially passing through the individual phases, the user is shaping the list of loops that are potential candidates for loop grafting. In the end, the actual in-silico insertion of the loop candidates from one protein to the other is performed and the results are visually presented to the user. In this way, the fully computational rational design of proteins and their loops results in newly designed protein structures that can be further assembled and tested through in-vitro experiments. LoopGrafter was designed in tight collaboration with protein engineers, and its final appearance reflects many testing iterations. We showcase the contribution of LoopGrafter on a real case scenario and provide the readers with the experts' feedback, confirming the usefulness of our tool.

Multiscale Visual Drilldown for the Analysis of Large Ensembles of Multi-Body Protein Complexes.

When studying multi-body protein complexes, biochemists use computational tools that can suggest hundreds or thousands of their possible spatial configurations. However, it is not feasible to experimentally verify more than only a very small subset of them. In this paper, we propose a novel multiscale visual drilldown approach that was designed in tight collaboration with proteomic experts, enabling a systematic exploration of the configuration space. Our approach takes advantage of the hierarchical structure of the data - from the whole ensemble of protein complex configurations to the individual configurations, their contact interfaces, and the interacting amino acids. Our new solution is based on interactively linked 2D and 3D views for individual hierarchy levels. At each level, we offer a set of selection and filtering operations that enable the user to narrow down the number of configurations that need to be manually scrutinized. Furthermore, we offer a dedicated filter interface, which provides the users with an overview of the applied filtering operations and enables them to examine their impact on the explored ensemble. This way, we maintain the history of the exploration process and thus enable the user to return to an earlier point of the exploration. We demonstrate the effectiveness of our approach on two case studies conducted by collaborating proteomic experts.

Visual Analysis of Protein-Protein Interaction Docking Models Using COZOID Tool.

Networks of protein-protein interactions (PPI) constitute either stable or transient complexes in every cell. Most of the cellular complexes keep their function, and therefore stay similar, during evolution. The evolutionary constraints preserve most cellular functions via preservation of protein structures and interactions. The evolutionary conservation information is utilized in template-based approaches, like protein structure modeling or docking. Here we use the combination of the template-free docking method with conservation-based selection of the best docking model using our newly developed COZOID tool.We describe a step-by-step protocol for visual selection of docking models, based on their similarity to the original protein complex structure. Using the COZOID tool, we first analyze contact zones of the original complex structure and select contact amino acids for docking restraints. Then we model and dock the homologous proteins. Finally, we utilize different analytical modes of our COZOID tool to select the docking models most similar to the original complex structure.

CAVER Analyst 2.0: analysis and visualization of channels and tunnels in protein structures and molecular dynamics trajectories.

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. Supplementary information: Supplementary data are available at Bioinformatics online.

COZOID: contact zone identifier for visual analysis of protein-protein interactions.

BACKGROUND: Studying the patterns of protein-protein interactions (PPIs) is fundamental for understanding the structure and function of protein complexes. The exploration of the vast space of possible mutual configurations of interacting proteins and their contact zones is very time consuming and requires the proteomic expert knowledge. RESULTS: In this paper, we propose a novel tool containing a set of visual abstraction techniques for the guided exploration of PPI configuration space. It helps proteomic experts to select the most relevant configurations and explore their contact zones at different levels of detail. The system integrates a set of methods that follow and support the workflow of proteomics experts. The first visual abstraction method, the Matrix view, is based on customized interactive heat maps and provides the users with an overview of all possible residue-residue contacts in all PPI configurations and their interactive filtering. In this step, the user can traverse all input PPI configurations and obtain an overview of their interacting amino acids. Then, the models containing a particular pair of interacting amino acids can be selectively picked and traversed. Detailed information on the individual amino acids in the contact zones and their properties is presented in the Contact-Zone list-view. The list-view provides a comparative tool to rank the best models based on the similarity of their contacts to the template-structure contacts. All these techniques are interactively linked with other proposed methods, the Exploded view and the Open-Book view, which represent individual configurations in three-dimensional space. These representations solve the high overlap problem associated with many configurations. Using these views, the structural alignment of the best models can also be visually confirmed. CONCLUSIONS: We developed a system for the exploration of large sets of protein-protein complexes in a fast and intuitive way. The usefulness of our system has been tested and verified on several docking structures covering the three major types of PPIs, including coiled-coil, pocket-string, and surface-surface interactions. Our case studies prove that our tool helps to analyse and filter protein-protein complexes in a fraction of the time compared to using previously available techniques.

Interactive exploration of ligand transportation through protein tunnels.

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.