Browsing by Author "Krone, Michael"

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Open Access
Interactive visual analysis of biomolecular simulations
(2015) Krone, Michael; Ertl, Thomas (Prof. Dr.)
Molecular dynamics simulations can give detailed insights into the properties of biomolecules on an atomistic level. Improvements in the domain of simulation codes as well as of the available hardware enable the simulation of invariably more complex molecular processes. Molecular simulation is therefore often described as a “computational microscope” that makes it possible to run experiments virtually and thus gain insights into the function of proteins and other biomolecules. Although molecular dynamics simulations have inherent restrictions, the results can partially be obtained more reproducible, reliable, and safely than using wet lab experiments. The scope of application ranges from fundamental questions like the formation of protein conformations or the effect of mutations to complex analyses like synthesis rates of biodiesel in biotechnology or drug binding in medicine. Visualization of the simulation results is an essential part of the interpretation of these virtual experiments. It is so to say the ocular of the computational microscope, which makes the data visible. Interactive visualization facilitates making discoveries, since it fosters an exploratory visual analysis of the data. Molecular models tailored to specific problems illustrate the particular properties of the visualized biomolecules. Examples are abstract representations that show the functional structure of a protein, or molecular surfaces that depict the contact surface between a molecule and a solvent. Available visualization techniques are, however, often not efficient enough to ensure an interactive exploration of large, dynamic data. A more comprehensive analysis that goes beyond this direct visualization of the simulation data is attained through feature extractions, which are executed as part of the visualization. Here, derived features of the simulated biomolecules like potential binding sites for reactants are extracted from the raw data, that is, the positions and elements of the atoms. Similar to the existing visualization techniques, previous analysis methods are in most cases not applicable in real-time and, thus, restricted to static data like single time steps of a simulation. For simulation data, however, processes that extend over a period of time can be of particular interest, for example conformational changes of a protein. Since the available feature extraction methods are not applicable in real-time, the results have to be precomputed for all time steps. Parameter changes imply a costly recalculation for the whole simulation. Hence, an exploratory visual analysis requires new methods that can be applied interactively to dynamic data. In this work, various methods that support the interactive visual analysis of biomolecular processes are introduced and discussed. The presented GPU-accelerated methods are parameterizable in real-time by the user and enable an exploratory analysis on current desktop computers. The basis for a visual exploration is the interactive visualization of complex molecular models for large, dynamic data sets without resorting to precomputed data. Consequently, this allows the user to switch between different representations without delay, which could otherwise disrupt and impair the analysis process. Hence, the user can analyze the dynamics of the simulated biomolecules. To facilitate the visual analysis, several real-time rendering methods have been developed and introduced, which for example enhance depth perception or provide a clearer, less cluttered depiction using non-photorealistic rendering. Based on these techniques, analysis methods and tools have been developed that extract complex properties of the simulated molecules. An example is the detection of cavities and channels, which play an essential role for the function of proteins. This enables analyzing the accessibility of binding sites for reactants, which can trigger an enzymatic reaction, or the permeability of a channel protein for a certain species of solvent molecules. Since the visual analysis of the simulation data is fully interactive, it supports the user not only in verifying existing hypotheses about the properties of the biomolecules but also allows for unexpected findings. Experts from the field of biochemistry have for example been able to find a channel to the binding site of a protein that did not agree with the predicted one. The combination of various analysis methods allows for a comprehensive, consistently interactive, exploratory visual analysis of biomolecular simulations, which gives users detailed insights into the data in real-time and fosters the discovery of new, unanticipated phenomena.
Open Access
Visual analysis of large‐scale protein‐ligand interaction data
(2021) Schatz, Karsten; Franco‐Moreno, Juan José; Schäfer, Marco; Rose, Alexander S.; Ferrario, Valerio; Pleiss, Jürgen; Vázquez, Pere‐Pau; Ertl, Thomas; Krone, Michael
When studying protein‐ligand interactions, many different factors can influence the behaviour of the protein as well as the ligands. Molecular visualisation tools typically concentrate on the movement of single ligand molecules; however, viewing only one molecule can merely provide a hint of the overall behaviour of the system. To tackle this issue, we do not focus on the visualisation of the local actions of individual ligand molecules but on the influence of a protein and their overall movement. Since the simulations required to study these problems can have millions of time steps, our presented system decouples visualisation and data preprocessing: our preprocessing pipeline aggregates the movement of ligand molecules relative to a receptor protein. For data analysis, we present a web‐based visualisation application that combines multiple linked 2D and 3D views that display the previously calculated data The central view, a novel enhanced sequence diagram that shows the calculated values, is linked to a traditional surface visualisation of the protein. This results in an interactive visualisation that is independent of the size of the underlying data, since the memory footprint of the aggregated data for visualisation is constant and very low, even if the raw input consisted of several terabytes.