1,721,143 research outputs found
A Massively Parallel CUDA Algorithm to Compute and Visualize the Solvent Excluded Surface for Dynamic Molecular Data
No full textThe interactive visualization of molecular surfaces can help users to understand the dynamic behavior of proteins in molecular dynamics simulations. These simulations play an important role in biochemical and pharmaceutical research, e.g. in drug design. The efficient calculation of molecular surfaces in a fast and memory-saving way is a challenging task. For example, to gain a detailed understanding of complex diseases like Alzheimer, conformational changes and spatial interactions between molecules have to be investigated. Molecular surfaces, such as Solvent Excluded Surfaces (SES), are instrumental for identifying structures such as tunnels or cavities that critically influence transport processes and docking events, which might induce enzymatic reactions. Therefore, we developed a highly parallelized algorithm that exploits the massive computing power of modern graphics hardware. Our analytical algorithm is suitable for the real-time computation of dynamic SES based on many time steps, as it runs interactively on a single consumer GPU for more than 20 k atoms.Workshop on Molecular Graphics and Visual Analysis of Molecular DataSession
MolVa 2023: Frontmatter
No full textWorkshop on Molecular Graphics and Visual Analysis of Molecular Dat
VisGap 2024: Frontmatter
No full textVisGap - The Gap between Visualization Research and Visualization Softwar
VisGap 2022: Frontmatter
No full textVisGap - The Gap between Visualization Research and Visualization Softwar
Analyzing Protein Similarity by Clustering Molecular Surface Maps
No full textMany biochemical and biomedical applications like protein engineering or drug design are concerned with finding functionally similar proteins, however, this remains to be a challenging task. We present a new imaged-based approach for identifying and visually comparing proteins with similar function that builds on the hierarchical clustering of Molecular Surface Maps. Such maps are two-dimensional representations of complex molecular surfaces and can be used to visualize the topology and different physico-chemical properties of proteins. Our method is based on the idea that visually similar maps also imply a similarity in the function of the mapped proteins. To determine map similarity we compute descriptive feature vectors using image moments, color moments, or a Convolutional Neural Network and use them for a hierarchical clustering of the maps. We show that image similarity as found by our clustering corresponds to functional similarity of mapped proteins by comparing our results to the BRENDA database, which provides a hierarchical function-based annotation of enzymes. We also compare our results to the TM-score, which is a similarity value for pairs of arbitrary proteins. Our visualization prototype supports the entire workflow from map generation, similarity computing to clustering and can be used to interactively explore and analyze the results.Eurographics Workshop on Visual Computing for Biology and MedicineProtein
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