2,601 research outputs found
Molecular simulations and visualization: introduction and overview
Here we provide an introduction and overview of current progress in the field of molecular simulation and visualization, touching on the following topics: (1) virtual and augmented reality for immersive molecular simulations; (2) advanced visualization and visual analytic techniques; (3) new developments in high performance computing; and (4) applications and model building
Techniques and algorithms for immersive and interactive visualization of large datasets
Advances in computing power have made it possible for scientists to perform atomistic simulations of material systems that range in size, from a few hundred thousand atoms to one billion atoms. An immersive and interactive walkthrough of such datasets is an ideal method for exploring and understanding the complex material processes in these simulations. However rendering such large datasets at interactive frame rates is a major challenge. A scalable visualization platform is developed that is scalable and allows interactive exploration in an immersive, virtual environment. The system uses an octree based data management system that forms the core of the application. This reduces the amount of data sent to the pipeline without a per-atom analysis. Secondary algorithms and techniques such as modified occlusion culling, multiresolution rendering and distributed computing are employed to further speed up the rendering process. The resulting system is highly scalable and is capable of visualizing large molecular systems at interactive frame rates on dual processor SGI Onyx2 with an InfinteReality2 graphics pipeline
Geometric algorithms for cavity detection on protein surfaces
Macromolecular structures such as proteins heavily empower cellular processes or functions.
These biological functions result from interactions between proteins and peptides,
catalytic substrates, nucleotides or even human-made chemicals. Thus, several
interactions can be distinguished: protein-ligand, protein-protein, protein-DNA,
and so on. Furthermore, those interactions only happen under chemical- and shapecomplementarity
conditions, and usually take place in regions known as binding sites.
Typically, a protein consists of four structural levels. The primary structure of a protein
is made up of its amino acid sequences (or chains). Its secondary structure essentially
comprises -helices and -sheets, which are sub-sequences (or sub-domains) of amino
acids of the primary structure. Its tertiary structure results from the composition of
sub-domains into domains, which represent the geometric shape of the protein. Finally,
the quaternary structure of a protein results from the aggregate of two or more
tertiary structures, usually known as a protein complex.
This thesis fits in the scope of structure-based drug design and protein docking. Specifically,
one addresses the fundamental problem of detecting and identifying protein
cavities, which are often seen as tentative binding sites for ligands in protein-ligand
interactions. In general, cavity prediction algorithms split into three main categories:
energy-based, geometry-based, and evolution-based. Evolutionary methods build upon
evolutionary sequence conservation estimates; that is, these methods allow us to detect
functional sites through the computation of the evolutionary conservation of the
positions of amino acids in proteins. Energy-based methods build upon the computation
of interaction energies between protein and ligand atoms. In turn, geometry-based algorithms
build upon the analysis of the geometric shape of the protein (i.e., its tertiary
structure) to identify cavities. This thesis focuses on geometric methods.
We introduce here three new geometric-based algorithms for protein cavity detection.
The main contribution of this thesis lies in the use of computer graphics techniques
in the analysis and recognition of cavities in proteins, much in the spirit of molecular
graphics and modeling. As seen further ahead, these techniques include field-of-view
(FoV), voxel ray casting, back-face culling, shape diameter functions, Morse theory,
and critical points. The leading idea is to come up with protein shape segmentation,
much like we commonly do in mesh segmentation in computer graphics. In practice,
protein cavity algorithms are nothing more than segmentation algorithms designed for
proteins.Estruturas macromoleculares tais como as proteínas potencializam processos ou funções
celulares. Estas funções resultam das interações entre proteínas e peptídeos, substratos
catalíticos, nucleótideos, ou até mesmo substâncias químicas produzidas pelo
homem. Assim, há vários tipos de interacções: proteína-ligante, proteína-proteína,
proteína-DNA e assim por diante. Além disso, estas interações geralmente ocorrem em
regiões conhecidas como locais de ligação (binding sites, do inglês) e só acontecem sob
condições de complementaridade química e de forma. É também importante referir que
uma proteína pode ser estruturada em quatro níveis. A estrutura primária que consiste
em sequências de aminoácidos (ou cadeias), a estrutura secundária que compreende
essencialmente por hélices e folhas , que são subsequências (ou subdomínios) dos
aminoácidos da estrutura primária, a estrutura terciária que resulta da composição de
subdomínios em domínios, que por sua vez representa a forma geométrica da proteína,
e por fim a estrutura quaternária que é o resultado da agregação de duas ou mais estruturas
terciárias. Este último nível estrutural é frequentemente conhecido por um
complexo proteico.
Esta tese enquadra-se no âmbito da conceção de fármacos baseados em estrutura e no
acoplamento de proteínas. Mais especificamente, aborda-se o problema fundamental
da deteção e identificação de cavidades que são frequentemente vistos como possíveis
locais de ligação (putative binding sites, do inglês) para os seus ligantes (ligands, do
inglês). De forma geral, os algoritmos de identificação de cavidades dividem-se em três
categorias principais: baseados em energia, geometria ou evolução. Os métodos evolutivos
baseiam-se em estimativas de conservação das sequências evolucionárias. Isto é,
estes métodos permitem detectar locais funcionais através do cálculo da conservação
evolutiva das posições dos aminoácidos das proteínas. Em relação aos métodos baseados
em energia estes baseiam-se no cálculo das energias de interação entre átomos
da proteína e do ligante. Por fim, os algoritmos geométricos baseiam-se na análise da
forma geométrica da proteína para identificar cavidades. Esta tese foca-se nos métodos
geométricos.
Apresentamos nesta tese três novos algoritmos geométricos para detecção de cavidades
em proteínas. A principal contribuição desta tese está no uso de técnicas de computação
gráfica na análise e reconhecimento de cavidades em proteínas, muito no espírito da
modelação e visualização molecular. Como pode ser visto mais à frente, estas técnicas
incluem o field-of-view (FoV), voxel ray casting, back-face culling, funções de diâmetro
de forma, a teoria de Morse, e os pontos críticos. A ideia principal é segmentar a
proteína, à semelhança do que acontece na segmentação de malhas em computação
gráfica. Na prática, os algoritmos de detecção de cavidades não são nada mais que
algoritmos de segmentação de proteínas
Interactive Visualization of Molecular Dynamics Simulation Data
Molecular Dynamics Simulations (MD) plays an essential role in the field of computational biology. The simulations produce extensive high-dimensional, spatio-temporal data describ-ing the motion of atoms and molecules. A central challenge in the field is the extraction and visualization of useful behavioral patterns from these simulations. Throughout this thesis, I collaborated with a computational biologist who works on Molecular Dynamics (MD) Simu-lation data. For the sake of exploration, I was provided with a large and complex membrane simulation. I contributed solutions to his data challenges by developing a set of novel visual-ization tools to help him get a better understanding of his simulation data. I employed both scientific and information visualization, and applied concepts of abstraction and dimensions projection in the proposed solutions. The first solution enables the user to interactively fil-ter and highlight dynamic and complex trajectory constituted by motions of molecules. The molecular dynamic trajectories are identified based on path length, edge length, curvature, and normalized curvature, and their combinations. The tool exploits new interactive visual-ization techniques and provides a combination of 2D-3D path rendering in a dual dimension representation to highlight differences arising from the 2D projection on a plane. The sec-ond solution introduces a novel abstract interaction space for Protein-Lipid interaction. The proposed solution addresses the challenge of visualizing complex, time-dependent interactions between protein and lipid molecules. It also proposes a fast GPU-based implementation that maps lipid-constituents involved in the interaction onto the abstract protein interaction space. I also introduced two abstract level-of-detail (LoD) representations with six levels of detail for lipid molecules and protein interaction. Finally, I proposed a novel framework consisting of four linked views: A time-dependent 3D view, a novel hybrid view, a clustering timeline, and a details-on-demand window. The framework exploits abstraction and projection to enable the user to study the molecular interaction and the behavior of the protein-protein interaction and clusters. I introduced a selection of visual designs to convey the behavior of protein-lipid interaction and protein-protein interaction through a unified coordinate system. Abstraction is used to present proteins in hybrid 2D space, and a projected tiled space is used to present both Protein-Lipid Interaction (PLI) and Protein-Protein Interaction (PPI) at the particle level in a heat-map style visual design. Glyphs are used to represent PPI at the molecular level. I coupled visually separable visual designs in a unified coordinate space. The result lets the user study both PLI and PPI separately, or together in a unified visual analysis framework
Dynamic Influence Networks for Rule-based Models
We introduce the Dynamic Influence Network (DIN), a novel visual analytics
technique for representing and analyzing rule-based models of protein-protein
interaction networks. Rule-based modeling has proved instrumental in developing
biological models that are concise, comprehensible, easily extensible, and that
mitigate the combinatorial complexity of multi-state and multi-component
biological molecules. Our technique visualizes the dynamics of these rules as
they evolve over time. Using the data produced by KaSim, an open source
stochastic simulator of rule-based models written in the Kappa language, DINs
provide a node-link diagram that represents the influence that each rule has on
the other rules. That is, rather than representing individual biological
components or types, we instead represent the rules about them (as nodes) and
the current influence of these rules (as links). Using our interactive DIN-Viz
software tool, researchers are able to query this dynamic network to find
meaningful patterns about biological processes, and to identify salient aspects
of complex rule-based models. To evaluate the effectiveness of our approach, we
investigate a simulation of a circadian clock model that illustrates the
oscillatory behavior of the KaiC protein phosphorylation cycle.Comment: Accepted to TVCG, in pres
I-Light Symposium 2005 Proceedings
I-Light was made possible by a special appropriation by the State of Indiana.
The research described at the I-Light Symposium has been supported by numerous grants from several sources.
Any opinions, findings and conclusions, or recommendations expressed in the 2005 I-Light Symposium Proceedings are those of the researchers and authors and do not necessarily reflect the views of the granting agencies.Indiana University Office of the Vice
President for Research and Information Technology, Purdue University Office of the
Vice President for Information Technology and CI
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