Comparative Visualization of Molecular Surfaces Using Deformable Models

Scharnowski, Katrin; Krone, Michael; Reina, Guido; Kulschewski, Tobias; Pleiss, Jürgen; Ertl, Thomas

doi:10.1111/cgf.12375

Cited by 15 publications

(6 citation statements)

References 48 publications

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“…Sarikaya et al also take into account the residue scale, by visualizing classifier performance with respect to protein chains on which a classifier has operated [SAMG14]. Finally, to communicate the differences of surface projected parameters, Scharnowski et al propose to use deformable models [SKR*14]. Our goal is to provide a compact representation that conveys a larger set of data features (e.g.…”

Section: Related Workmentioning

confidence: 99%

Visual Analysis of protein‐ligand interactions

Vázquez

Hermosilla²,

Guallar

et al. 2018

Computer Graphics Forum

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Figure 1: Two examples of our molecular interactions visualization. The left image shows the initial inspection of the secondary structures and the residues in the protein. By hovering on a secondary structure (top right) or residue (bottom right), we obtain the details. The right image shows the energy inspection mode, with the ligand shown in the center of the display. Here, the user can see both aggregate information, in the form of the maximum energies reached along the simulation path and inspect details about individual steps, such as the energy components (i. e. van der Waals, Coulomb Solvent, and Coulomb Vacuum) of the interaction energy (by hovering onto the energy bars) or the intra-molecular bond that is formed (in orange) at this step of the simulation. AbstractThe analysis of protein-ligand interactions is complex because of the many factors at play. Most current methods for visual analysis provide this information in the form of simple 2D plots, which, besides being quite space hungry, often encode a low number of different properties. In this paper we present a system for compact 2D visualization of molecular simulations. It purposely omits most spatial information and presents physical information associated to single molecular components and their pairwise interactions through a set of 2D InfoVis tools with coordinated views, suitable interaction, and focus+context techniques to analyze large amounts of data. The system provides a wide range of motifs for elements such as protein secondary structures or hydrogen bond networks, and a set of tools for their interactive inspection, both for a single simulation and for comparing two different simulations. As a result, the analysis of protein-ligand interactions of Molecular Simulation trajectories is greatly facilitated.

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Section: Related Workmentioning

confidence: 99%

Visual Analysis of protein‐ligand interactions

Vázquez

Hermosilla²,

Guallar

et al. 2018

Computer Graphics Forum

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show abstract

“…Sarikaya et al also take into account the residue scale, by visualizing classifier performance with respect to protein chains on which a classifier has operated [42]. Finally, to communicate the differences of surface projected parameters, Scharnowski et al propose to use deformable models [44]. 2D molecular interaction visualization.…”

Section: Related Workmentioning

confidence: 99%

Physics-Based Visual Characterization of Molecular Interaction Forces

Hermosilla

Estrada

Guallar

et al. 2017

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. Successive steps during the visual analysis of the binding nature of Aspirin and the Phospholipase A2 protein. We compute and visualize all essential interaction energies represented by 2D and 3D arrows. The orientation of the depicted arrows encodes the sign of the energy, i.e., attracting vs. repelling force. The width of the arrows as well as the color of the residue's silhouettes support energy quantification. During the visual analysis, energies are computed and depicted on-the-fly to support interactive hypothesis testing (left), and residues can be filtered based on energy and distance to obtain a more focused view (middle). Additionally, a 2D visualization helps to obtain total energy values in an uncluttered manner (right).Abstract-Molecular simulations are used in many areas of biotechnology, such as drug design and enzyme engineering. Despite the development of automatic computational protocols, analysis of molecular interactions is still a major aspect where human comprehension and intuition are key to accelerate, analyze, and propose modifications to the molecule of interest. Most visualization algorithms help the users by providing an accurate depiction of the spatial arrangement: the atoms involved in inter-molecular contacts. There are few tools that provide visual information on the forces governing molecular docking. Unfortunately these tools, commonly restricted to close interaction between atoms, do not consider whole simulation paths, long-range distances and, importantly, do not provide visual cues for a quicker and intuitive comprehension of the energy functions (modeling intermolecular interactions) involved. In this paper, we propose visualizations designed to enable the characterization of interaction forces by taking into account several relevant variables such as molecule-ligand distance and the energy function, which is essential to understand binding affinities. We put emphasis on mapping molecular docking paths obtained from Molecular Dynamics or Monte Carlo simulations, and provide time-dependent visualizations for different energy components and particle resolutions: atoms, groups or residues. The presented visualizations have the potential to support domain experts in a more efficient drug or enzyme design process.

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“…It, however, has difficulty in handling objects with different topologies. DFI approaches, on the other hand, represent the shape as an isosurface, which does not require dense surface correspondences and allows topological change without special treatment. However, DFI approaches are computationally intensive, which are thus less suitable for practical application.…”

Section: Related Workmentioning

confidence: 99%

Detail‐preserving SPH fluid control with deformation constraints

Feng

Liu

2017

Computer Animation & Virtual

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It is challenging to drive particle-based smoothed-particle hydrodynamics fluid to match the target shape and the deforming fluid shape between different models smoothly, especially when the natural fluid motion must be preserved. To achieve the desired behavior, we first generate control particles by sampling the target shapes and then apply a deformation constraint to each control particle, with its neighboring fluid particles keeping details within its influence region. For the generation of control particles, we classify input models into source object and target object, then separately sample them by voxelization method, and generate source control particles and target control particles, respectively. Our deformation constraint includes two parts. In the first part, we deform the source control model to the target control model according to specific space point correspondence between source control particles and target control particles; then, fluid particles are attracted by control particles and complete deformation between different shapes. In the second part, to reduce the lacking of fluid details when fluid deforms, we introduce a new control energy transfer mechanism for control particles. This deformation constraint is solved under smoothed-particle hydrodynamics-based fluid simulation framework, which makes our simulation fast, robust, and well suitable for interactive applications. Various experiments demonstrated the effectiveness of our method.

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Comparative Visualization of Molecular Surfaces Using Deformable Models

Cited by 15 publications

References 48 publications

Visual Analysis of protein‐ligand interactions

Visual Analysis of protein‐ligand interactions

Physics-Based Visual Characterization of Molecular Interaction Forces

Detail‐preserving SPH fluid control with deformation constraints

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