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.

Molecular dynamics simulations of proteins play a growing role in various fields such as pharmaceutical, biochemical and medical research. Accordingly, the need for high quality visualization of these protein systems raises. Highly interactive visualization techniques are especially needed for the analysis of time-dependent molecular simulations. Beside various other molecular representations the surface representations are of high importance for these applications. So far, users had to accept a trade-off between rendering quality and performance--particularly when visualizing trajectories of time-dependent protein data. We present a new approach for visualizing the Solvent Excluded Surface of proteins using a GPU ray casting technique and thus achieving interactive frame rates even for long protein trajectories where conventional methods based on precomputation are not applicable. Furthermore, we propose a semantic simplification of the raw protein data to reduce the visual complexity of the surface and thereby accelerate the rendering without impeding perception of the protein's basic shape. We also demonstrate the application of our Solvent Excluded Surface method to visualize the spatial probability density for the protein atoms over the whole period of the trajectory in one frame, providing a qualitative analysis of the protein flexibility.

Abstract Water is known to play a crucial role in protein structure, flexibility and activity. The use of molecular dynamics simulations allows detailed studies of complex protein‐solvent interactions. Cluster analysis and density‐based approaches have been successfully used for the identification and analysis of conserved water molecules and hydration patterns of proteins. However, appropriate tools for analysing long‐time molecular dynamics simulations with respect to tracking and visualising the paths of solvent molecules are lacking. Our method focuses on visualising the solvent paths entering and leaving cavities of the protein and allows to study the route and dynamics of the exchange of tightly bound internal water molecules with the bulk solvent. The proposed visualisation also represents dynamic properties such as direction and velocity in the solvent. Especially, by clustering similar path‐lines with respect to designated properties the visualisation can be abstracted to represent the principal paths of solvent molecules through the cavities. Its application in the analysis of long‐time scale molecular dynamics simulations not only confirmed conjectures based on previous manual observations made by chance, but also led to novel insights into the dynamical and structural role of water molecules and its interplay with protein structure.

We propose a new approach for the visualization of hyperstreamlines, which offers potential for better scalability than the conventional polygon-based approach. Our method circumvents the bandwidth bottleneck between the CPU and GPU by transmitting a small set of parameters for each tube segment and generates the surface directly on the GPU using the classical sphere tracing approach. This reduces the load on the CPU that would otherwise need to provide a suitable level-of-detail representation of the scene, while offering even higher quality in the resulting surfaces since every fragment is traced individually. We demonstrate the effectiveness of this approach by comparing it to the performance and output of conventional visualization tools in the application area of diffusion tensor imaging of human brain MR scans. The method presented here can also be utilized to generate other types of surfaces on the GPU that are too complex to handle with direct ray casting and can therefore be adapted for other applications.

Virtual prototyping is increasingly replacing real mock-ups and experiments in industrial product development. Part of this process is the simulation of structural and functional properties, which is in many cases based on finite element analysis (FEA). One prominent example from the automotive industry is the safety improvement resulting from crash worthiness simulations. A simulation model for this purpose usually consists of up to one million finite elements and is assembled from many parts, which are individually meshed out of their CAD representation. In order to accelerate the development cycle, simulation engineers want to be able to modify their FE models without going back to the CAD department. Furthermore, valid CAD models might even not be available in preliminary design stages. However, in contrast to CAD, there is a lack of tools that offer the possibility of modification and processing of finite element components while maintaining the properties relevant to the simulation. In this application paper we present interactive algorithms for intuitive and fast editing of FE models and appropriate visualization techniques to support engineers in understating these models. This includes new kinds of manipulators, feedback mechanisms and facilities for virtual reality and immersion at the workplace, e.g. autostereoscopic displays and haptic devices.