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Four generations of Nvidia graphics cards. Comparison of critical parameters for four graphics card generations (based on information available on the Nvidia website: ). The number of cores provides information on potential parallelization. Memory and bandwidth are important as they govern the amount of data that can efficiently be passed between CPU and GPU. Processing power provides an indication of hardware performance measured in Giga flops (Gflops). The term flops is the abbreviation of FLoating point OPerations per Second which is related to the number of instructions per second.
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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-mic...
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... This property is particularly important for protein function or for adaptation of therapeutic molecules to a binding site. However, techniques such as X-ray crystallography, used to determine molecular structures, depict static objects. In contrast, molecular dynamics simulations are widely used to investigate molecular functions and generate dynamic data. O’Donoghue et al. [1] describe programs to create and visualize molecular motions and propose to superimpose several dynamic snapshots. This method is limited in the number of snapshots that can be taken into account in order to avoid over- loading the scene and still obtain a comprehensible representation. An interesting method to represent the uncertainty in atomic positions without over- crowding the scene is to use blur effects [43, 44]. These methods are particularly well suited to represent metastable conformations of molecules or superimpose docking ligand poses [45]. The method of Lee and Varshney [43] is based on using multi-layered transparent surfaces to create the blur effect while the method of Schmidt-Ehrenberg et al. , is based on volume rendering [44]. Another application of volumetric representations was recently presented by Phillips et al. , where the molecule itself is rendered as a blurry density gradient, providing the context for the cavities, which are extracted by seg- menting the volume [46]. Blur effects can also emphasize the depth of field. Falk et al. have applied depth blur in combination with colour desaturation [47], thereby drawing the attention of the user to a region of interest, while simultaneously clarifying the depth complexity of the scene (Figure 5C). These techniques were used on a sim- plified visualization of signal transduction in a cell (consisting of cylinders representing the cytoskeleton and spheres representing the signal proteins) and can also be applied to more detailed molecular models. New graphics card capabilities open the door for improving illustrative rendering. Recently, Weber added texture mapping onto ribbon representations thus creating some appealing pictures [48]. Beyond this illustrative and artistic goal, texture mapping can be used to annotate molecular representations. For example, Cipriano et al. , used little patches on molecular surfaces to enhance the visibility of ligand binding sites or to highlight a specific area on a surface [35] (see Figure 4D and Method6 in Table 1). Such a mapping procedure preserves other surface annotations that may for example be colour coded. Furthermore, the user can manually define a particular point of interest where a patch will be applied [35]. Recent approaches to annotate protein surfaces (Figure 5D) include text scaffolds [49], i.e. a smoothed invisible surface, used to position the text (see Method7 in Table 1). This technique is particularly well suited to annotate binding sites as the label is not just anchored to a single point, but follows the molecular topology. The obvious benefits are a better visibility and positioning of the text, thus avoiding some problems of traditional annotations, typically located in screen space, where labels may be hidden depending on the orientation of the scene. In less than a decade, substantial progress was achieved in molecular visualization. Many new programs draw benefit from the latest capabilities of graphics cards, yet may not work correctly on older hardware and basic laptop computers. To be more specific, any GPU supporting at least the Shader Model 3.0 (indicated in the vendor specifica- tions) meets all the technical requirements. As a guideline for the minimum hardware prerequisites, we have listed the equipment used in recently described methodologies (Table 2). Given the rapid evolution of the graphics hardware, many of these configurations start to be obsolete. As a guideline for choosing which graphics card to use at the time of writing, a basic configuration could consist in a medium grade graphics card such as the Nvidia GTX 460 (approximately $230), and a higher end configuration in an Nvidia GTX 480 or 580 (approximately $600). We note that it is not necessary to use a professional card such as the Nvidia Quadro series (clearly more expensive than the previous models) for the purposes described in this article, except for stereo rendering (not discussed in this review, but supported by some programs). Here, we focus on Nvidia cards, as many methods use code dedicated to hardware from this vendor (i.e. based on Cg or CUDA). Concerning the CPU, a basic configuration could be composed by an Intel quad-core i7-870 2.93 GHz (approximately $330) or, for a high performance machine, by an Intel hexa-core i7-7980x 3.33 GHz (approximately $1200). It is difficult to make a detailed prediction about the evolution of such hardware, as it largely depends on global marketing strategies of hardware constructors. Yet, there has been a big increase in graphics card capacities these last generations (Figure 1) and this trend will probably continue. This progress is driven by new GPU uses such as general calculations and will benefit graphics computation as well. Thus, in the new virtual visualization world, scientists will be able to efficiently interact with molecular structures rather than merely display an elaborate pre-calculated picture—which may induce misconceptions [50]. Enabling such technol- ogies raises issues of sharing, organizing and annotat- ing visualizations of molecular structures and related data for efficient collaboration among scientists. Emerging collaborative virtual environments offer possible solutions [51], but their wide adoption remains a challenge for the near future. A related area concerns managing provenance of molecular visualizations with tools such as VisTrails ( .vistrails.org/). Thanks to close collaboration between molecular scientists and visualization experts, prototypes of such virtual worlds already exist in computer scientists’ labs and may soon become available to the whole scientific community. Cel-shading: the full term is celluloid shading, sometimes also called toon-shading. It is a lighting technique that is qualified as non-photorealistic. The goal of cel-shading is to obtain a ‘cartoon’-like picture. For this purpose, the colour panel is limited and the shadows are not based on a gradient but are changed as a function of cut-off values, hence creating clear shadow frontiers. Furthermore, object contours are outlined to create an effect as if ‘drawn by hand’. Central Processing Unit (CPU): computer component that executes the instructions of an informatics program. CPUs are often designed for general purpose calculations. Clock speed: speed at which a microprocessor executes instructions. Colour desaturation: this effect diminishes colour intensity, often in order to highlight specific parts of a scene. If colours are totally desaturated, a grey-scale image is obtained. Core: The core is the part of a CPU or GPU processor that actually reads and executes instructions. Processors can have single or multiple-core architectures. At time of writing, CPUs exist with 1 core (single core), 2 cores (dual cores), 4 cores (quad cores) and more recently 6 cores (hexa cores) or 8 cores (octo cores). For GPUs, the number of cores is much greater (up to 480 cores), but these cores are clearly dedicated to more specific operations compared to the general purpose CPU cores. Depth cueing: colour effect to improve depth perception. The overall idea is to alter the actual object colour to increasingly match the background colour as a function of the distance from the camera. The colour of an object far away from the camera will be very similar to the background colour. This effect is sometimes referred to as ‘fog effect’. Display rate: measured in frames per second (fps), is the number of images that can be displayed per second. The higher this number, the better the user’s perception of the fluidity of the scene. A good frame rate is around 30 fps, as the human brain is only able to perceive up to about 25 images per second (called the persistence of vision). If such high display rates are achieved, the rendering is qualified as real time. Below 30 and above 10 fps, the rendering is qualified as interactive, because the scene is still more or less fluid, with some apparent latencies. For values less than 10 fps, the scene suffers from very noticeable decelerations. Frame rate: equivalent to display rate (see above). Geometry shader: a shader is a set of software instructions used to calculate rendering effects and sent to the graphics pipeline (see definition hereafter). There are three types of shaders: the geometry shader, the vertex shader (used when vertices are computed) and the pixel/fragment shader (used when pixels are computed). The geometry shader is used to generate graphic primitives such as points, lines or triangles. Graphics pipeline: this term refers to the ensem- ble of steps of GPU treatments necessary to create a final image. First, the graphics pipeline processes vertices that can be assembled into primitives such as points, lines, triangles or polygons. During the ras- terization step, these primitives are transformed in discrete parts called fragments. Finally, these fragments will be converted into pixels used to form the final image. Graphics Processing Units (GPUs): component of a graphics card that executes instructions of an informatics program. Until recently, GPUs were dedicated to display operations and graphics data manipulations. Marching cubes algorithm: algorithm that recreates a triangulated object from a set of discrete points. It is an iso-surface approximation. Mesh: approximation of the three-dimensional shape of an object by polygons. Metaballs: graphics technique often used to represent ‘‘organic’’ objects or fluids using polynomial functions. Mixed complex: Mathematical structure mixing Delaunay tetrahedralization and its ...
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... of macromolecular structures is described in a recent review focusing on traditional approaches to visualize 3D structural data, providing an excellent overview of this field and currently available tools [1]. Here, we take a look at the latest contributions from the computer science field, with the potential to change the future of molecular visualization. Yet, many of these new approaches remain largely unknown, which could be explained by two main reasons: More (1) Common generally, interest very few focuses recent reviews on available address new and computer readily usable science tools. developments Those are only for rarely molecular asso- graphics. ciated The with article the work by Goddard of computer and scientists, Ferrin is one mostly of such geared rare reports towards [2]. Furthermore, new state-of-the-art the computer techniques visualization rather field than evolves providing very quickly end-user due to continuously software. Most renewed of the graphics results hardware presented capabil- in this ities. review Recently, relate the to ongoing performance developments of graphics (i.e. cards the has drastically corresponding increased tools by are a not factor generally of approximately available), 2.6 but over some the past notable 4 years. exceptions In order to exist illustrate and this are evolution, already we offered will to discuss the scientific some features community. of the In graphics Table 1, cards we from provided Nvidia, a one compilation of the main of fabri- these cants. tools; A big and leap forward occurred between the past two (2) A generations gap subsists (GTX between 2xx the and computer GTX science 4xx series) and compared bioinformatics to the fields. previous Publications ones (Figure 1). in the former The main often processing contain power very of detailed these graphics technical processing explan- units ations, ( GPUs rendering ) 1 benefits them from difficult the constantly to read rising for clock non-specialists speeds and is in largely the field. driven We tried by increasing to reduce the number of such expressions in the present manuscript and the remaining technical terms are described in a glossary at the end of the main text. Our approach is to stress the enor- mous potential of new visualization methods for structural biology and bioinformatics rather than describe the intrinsics of the techniques applied to obtain such visual effects. More generally, very few recent reviews address new computer science developments for molecular graphics. The article by Goddard and Ferrin is one of such rare reports [2]. Furthermore, the computer visualization field evolves very quickly due to continuously renewed graphics hardware capabilities. Recently, the performance of graphics cards has drastically increased by a factor of approximately 2.6 over the past 4 years. In order to illustrate this evolution, we will discuss some features of the graphics cards from Nvidia, one of the main fabri- cants. A big leap forward occurred between the past two generations (GTX 2xx and GTX 4xx series) compared to the previous ones (Figure 1). The main processing power of these graphics processing units ( GPUs ) 1 benefits from the constantly rising clock speeds and is largely driven by increasing parallelism. While modern CPUs only have up to six cores , GPUs can have up to 480 smaller cores (see ref. [3] for more details on GPU architecture). In addition to graphics output and geometry generation, modern GPUs can be used for general purpose calculations [3–5]. This tremendous potential encouraged computer scientists to design new algorithms for massively parallel execution on the GPU. However, using such hardware implies to comply with several constraints. GPU cores are dedicated to a limited set of specific, massively parallel operations. Another bottleneck is the com- munication between CPU and GPU, imposing limits on the transfer of large amounts of data. Fortunately, the memory bandwidth has increased consequently these past years (Figure 1). To help developers create GPU-optimized algorithms, specific formalisms such as the GLSL [6] (graphic card independent), Cg [7] (Nvidia card dependent), CUDA [8] (Nvidia card dependent) or OpenCL [9] (graphic card independent) languages were created. GLSL and Cg are dedicated for rendering, whereas CUDA and OpenCL are intended for calculations. Structural biology is another field where tremendous progress was achieved over the past decade. Experimentalists have developed new techniques to routinely crystallize proteins [10–12], increasing the number of available structures in databases. Using experimental techniques (such as electron- microscopy or small angle X-ray scattering) or computational methods (such as protein docking), it is now possible to study huge macromolecular structures such as chaperone proteins [13], the ribosome [14] or viral capsids [15] in atomic detail. Displaying such complex macromolecules requires efficient tools, an area where new scientific visualization techniques could provide a promising answer. These techniques furthermore provide an improved visual perception, which could drastically impact the way to visualize—and consequently to think about—mo- lecular structures. Backed by these observations, we endeavour to highlight the latest frontier research in the field and complement previous reports by presenting new developments originating in computer science and ...
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... of macromolecular structures is described in a recent review focusing on traditional approaches to visualize 3D structural data, providing an excellent overview of this field and currently available tools [1]. Here, we take a look at the latest contributions from the computer science field, with the potential to change the future of molecular visualization. Yet, many of these new approaches remain largely unknown, which could be explained by two main reasons: More (1) Common generally, interest very few focuses recent reviews on available address new and computer readily usable science tools. developments Those are only for rarely molecular asso- graphics. ciated The with article the work by Goddard of computer and scientists, Ferrin is one mostly of such geared rare reports towards [2]. Furthermore, new state-of-the-art the computer techniques visualization rather field than evolves providing very quickly end-user due to continuously software. Most renewed of the graphics results hardware presented capabil- in this ities. review Recently, relate the to ongoing performance developments of graphics (i.e. cards the has drastically corresponding increased tools by are a not factor generally of approximately available), 2.6 but over some the past notable 4 years. exceptions In order to exist illustrate and this are evolution, already we offered will to discuss the scientific some features community. of the In graphics Table 1, cards we from provided Nvidia, a one compilation of the main of fabri- these cants. tools; A big and leap forward occurred between the past two (2) A generations gap subsists (GTX between 2xx the and computer GTX science 4xx series) and compared bioinformatics to the fields. previous Publications ones (Figure 1). in the former The main often processing contain power very of detailed these graphics technical processing explan- units ations, ( GPUs rendering ) 1 benefits them from difficult the constantly to read rising for clock non-specialists speeds and is in largely the field. driven We tried by increasing to reduce the number of such expressions in the present manuscript and the remaining technical terms are described in a glossary at the end of the main text. Our approach is to stress the enor- mous potential of new visualization methods for structural biology and bioinformatics rather than describe the intrinsics of the techniques applied to obtain such visual effects. More generally, very few recent reviews address new computer science developments for molecular graphics. The article by Goddard and Ferrin is one of such rare reports [2]. Furthermore, the computer visualization field evolves very quickly due to continuously renewed graphics hardware capabilities. Recently, the performance of graphics cards has drastically increased by a factor of approximately 2.6 over the past 4 years. In order to illustrate this evolution, we will discuss some features of the graphics cards from Nvidia, one of the main fabri- cants. A big leap forward occurred between the past two generations (GTX 2xx and GTX 4xx series) compared to the previous ones (Figure 1). The main processing power of these graphics processing units ( GPUs ) 1 benefits from the constantly rising clock speeds and is largely driven by increasing parallelism. While modern CPUs only have up to six cores , GPUs can have up to 480 smaller cores (see ref. [3] for more details on GPU architecture). In addition to graphics output and geometry generation, modern GPUs can be used for general purpose calculations [3–5]. This tremendous potential encouraged computer scientists to design new algorithms for massively parallel execution on the GPU. However, using such hardware implies to comply with several constraints. GPU cores are dedicated to a limited set of specific, massively parallel operations. Another bottleneck is the com- munication between CPU and GPU, imposing limits on the transfer of large amounts of data. Fortunately, the memory bandwidth has increased consequently these past years (Figure 1). To help developers create GPU-optimized algorithms, specific formalisms such as the GLSL [6] (graphic card independent), Cg [7] (Nvidia card dependent), CUDA [8] (Nvidia card dependent) or OpenCL [9] (graphic card independent) languages were created. GLSL and Cg are dedicated for rendering, whereas CUDA and OpenCL are intended for calculations. Structural biology is another field where tremendous progress was achieved over the past decade. Experimentalists have developed new techniques to routinely crystallize proteins [10–12], increasing the number of available structures in databases. Using experimental techniques (such as electron- microscopy or small angle X-ray scattering) or computational methods (such as protein docking), it is now possible to study huge macromolecular structures such as chaperone proteins [13], the ribosome [14] or viral capsids [15] in atomic detail. Displaying such complex macromolecules requires efficient tools, an area where new scientific visualization techniques could provide a promising answer. These techniques furthermore provide an improved visual perception, which could drastically impact the way to visualize—and consequently to think about—mo- lecular structures. Backed by these observations, we endeavour to highlight the latest frontier research in the field and complement previous reports by presenting new developments originating in computer science and ...
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... of macromolecular structures is described in a recent review focusing on traditional approaches to visualize 3D structural data, providing an excellent overview of this field and currently available tools [1]. Here, we take a look at the latest contributions from the computer science field, with the potential to change the future of molecular visualization. Yet, many of these new approaches remain largely unknown, which could be explained by two main reasons: More (1) Common generally, interest very few focuses recent reviews on available address new and computer readily usable science tools. developments Those are only for rarely molecular asso- graphics. ciated The with article the work by Goddard of computer and scientists, Ferrin is one mostly of such geared rare reports towards [2]. Furthermore, new state-of-the-art the computer techniques visualization rather field than evolves providing very quickly end-user due to continuously software. Most renewed of the graphics results hardware presented capabil- in this ities. review Recently, relate the to ongoing performance developments of graphics (i.e. cards the has drastically corresponding increased tools by are a not factor generally of approximately available), 2.6 but over some the past notable 4 years. exceptions In order to exist illustrate and this are evolution, already we offered will to discuss the scientific some features community. of the In graphics Table 1, cards we from provided Nvidia, a one compilation of the main of fabri- these cants. tools; A big and leap forward occurred between the past two (2) A generations gap subsists (GTX between 2xx the and computer GTX science 4xx series) and compared bioinformatics to the fields. previous Publications ones (Figure 1). in the former The main often processing contain power very of detailed these graphics technical processing explan- units ations, ( GPUs rendering ) 1 benefits them from difficult the constantly to read rising for clock non-specialists speeds and is in largely the field. driven We tried by increasing to reduce the number of such expressions in the present manuscript and the remaining technical terms are described in a glossary at the end of the main text. Our approach is to stress the enor- mous potential of new visualization methods for structural biology and bioinformatics rather than describe the intrinsics of the techniques applied to obtain such visual effects. More generally, very few recent reviews address new computer science developments for molecular graphics. The article by Goddard and Ferrin is one of such rare reports [2]. Furthermore, the computer visualization field evolves very quickly due to continuously renewed graphics hardware capabilities. Recently, the performance of graphics cards has drastically increased by a factor of approximately 2.6 over the past 4 years. In order to illustrate this evolution, we will discuss some features of the graphics cards from Nvidia, one of the main fabri- cants. A big leap forward occurred between the past two generations (GTX 2xx and GTX 4xx series) compared to the previous ones (Figure 1). The main processing power of these graphics processing units ( GPUs ) 1 benefits from the constantly rising clock speeds and is largely driven by increasing parallelism. While modern CPUs only have up to six cores , GPUs can have up to 480 smaller cores (see ref. [3] for more details on GPU architecture). In addition to graphics output and geometry generation, modern GPUs can be used for general purpose calculations [3–5]. This tremendous potential encouraged computer scientists to design new algorithms for massively parallel execution on the GPU. However, using such hardware implies to comply with several constraints. GPU cores are dedicated to a limited set of specific, massively parallel operations. Another bottleneck is the com- munication between CPU and GPU, imposing limits on the transfer of large amounts of data. Fortunately, the memory bandwidth has increased consequently these past years (Figure 1). To help developers create GPU-optimized algorithms, specific formalisms such as the GLSL [6] (graphic card independent), Cg [7] (Nvidia card dependent), CUDA [8] (Nvidia card dependent) or OpenCL [9] (graphic card independent) languages were created. GLSL and Cg are dedicated for rendering, whereas CUDA and OpenCL are intended for calculations. Structural biology is another field where tremendous progress was achieved over the past decade. Experimentalists have developed new techniques to routinely crystallize proteins [10–12], increasing the number of available structures in databases. Using experimental techniques (such as electron- microscopy or small angle X-ray scattering) or computational methods (such as protein docking), it is now possible to study huge macromolecular structures such as chaperone proteins [13], the ribosome [14] or viral capsids [15] in atomic detail. Displaying such complex macromolecules requires efficient tools, an area where new scientific visualization techniques could provide a promising answer. These techniques furthermore provide an improved visual perception, which could drastically impact the way to visualize—and consequently to think about—mo- lecular structures. Backed by these observations, we endeavour to highlight the latest frontier research in the field and complement previous reports by presenting new developments originating in computer science and ...
Citations
... In the 1990s, the popularity of internet technology fostered the development of web-based molecular visualization tools [8]. GPU-accelerated graphics generation and rendering, as well as large-scale parallel computing, have played a crucial role in improving real-time interactive visualization effects, including geometric primitive rendering, occlusion culling, and lighting models [9]. ...
The structural characteristics of biomolecules are a major focus in the field of structural biology. Molecular visualization plays a crucial role in displaying structural information in an intuitive manner, aiding in the understanding of molecular properties. This paper provides a comprehensive overview of core concepts, key techniques, and tools in molecular visualization. Additionally, it presents the latest research findings to uncover emerging trends and highlights the challenges and potential directions for the development of the field.
... The visual inspection of different macromolecular systems is now more accessible than ever with the availability of a wide range of advanced software tools (Olson, 2018). The different software platforms and the increased computer power have allowed the scientists to visualize, study, understand complex structures (Chavent et al., 2011). ...
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... More recently, parts of hyperboloids have been used to render molecular surfaces and other representations [6,7]. Rendering of all these representations is now accelerated by GPU functions [8]. While the recent trend has been toward increasingly smooth molecular representations, tetrahedra have been proposed as the basis for representing molecules at various scales [9]. ...
We discuss how design enriches molecular science, particularly structural biology and bioinformatics. We present two use cases, one in academic practice and the other to design for outreach. The first case targets the representation of ion channels and their dynamic properties. In the second, we document a transition process from a research environment to general-purpose designs. Several testimonials from practitioners are given. By describing the design process of abstracted shapes, exploded views of molecular structures, motion-averaged slices, 360-degree panoramic projections, and experiments with lit sphere shading, we document how designers help make scientific data accessible without betraying its meaning, and how a creative mind adds value over purely data-driven visualizations. A similar conclusion was drawn for public outreach, as we found that comic-book-style drawings are better suited for communicating science to a broad audience.
... The visual inspection of different macromolecular systems is now more accessible than ever with the availability of a wide range of advanced software tools (Olson, 2018). The different software platforms and the increased computer power have allowed the scientists to visualize, study, understand complex structures (Chavent et al., 2011). ...
... In recent years, structural biology has become increasingly interconnected with many other kinds of data (Im et al., 2016), and the visualization challenges have moved from the static views of single molecules toward dynamic views of much larger scales, such as whole viruses, subcellular organelles, or even entire cells (see also Goodsell et al., 2018). These challenges have inspired intense research within the computer graphics community, aimed at creating solutions that take better advantage of current graphic processor unit (GPU) capabilities (Chavent et al., 2011), as well as new analysis and immersive approaches (Hirst et al., 2014). These recent developments are summarized in three state-of-the-art reviews of molecular graphics, each describing technical developments that can help structural biologists choose the best algorithms for a dedicated purpose. ...
Visualization of molecular structures is one of the most common tasks carried out by structural biologists, typically using software, such as Chimera, COOT, PyMOL, or VMD. In this Perspective article, we outline how past developments in computer graphics and data visualization have expanded the understanding of biomolecular function, and we summarize recent advances that promise to further transform structural biology. We also highlight how progress in molecular graphics has been impeded by communication barriers between two communities: the computer scientists driving these advances, and the structural and computational biologists who stand to benefit. By pointing to canonical papers and explaining technical progress underlying new graphical developments in simple terms, we aim to improve communication between these communities; this, in turn, would help shape future developments in molecular graphics.
... projecting a PyMOL session) or static slide, both of which are herein referred to as 2D-display, much of the structural richness is reduced. In such displays distance and perspective are conveyed by visual effects such as depth cueing (shading), occlusion, and foreshortening, all of which are exemplified in Fig. 1 [14]. In dynamic sessions, these aspects are further enhanced by motion cues that help the user orient themselves [14]. ...
... In such displays distance and perspective are conveyed by visual effects such as depth cueing (shading), occlusion, and foreshortening, all of which are exemplified in Fig. 1 [14]. In dynamic sessions, these aspects are further enhanced by motion cues that help the user orient themselves [14]. Even with the aide of these visual cues, intuiting distances and angles is still difficult for the majority of viewers which is further exacerbated when information is left out to aide clarity (resecting residues/structures to show a specific point of interest). ...
In drug discovery, structural knowledge of a target enables structure-based design approaches and thereby reduces the time and labor required to develop a therapy. Whilst molecular graphics frameworks coupled with computational analysis are now ubiquitous tools for the structural and computational biologist, sharing the detailed visualization and derived structural information with non-expert users still presents a challenge. Here we describe an intuitive virtual world for viewing, manipulating, and modifying chemical and macromolecular structures in a fully immersive and collaborative 3D environment. By reducing the barriers to viewing and interacting with structural data, structural analysis can be democratized to a general scientist, which in turn fosters novel collaboration, ideas, and findings in structural biology and structure-based drug discovery.
... Numerous representations are available for the depiction of MD data from simple molecules to complex macromolecular systems [1,13]. Protein molecules, for example, are often represented by surfaces [5,14,20] while small molecules can be depicted as licorice, ball-and-stick or Van der Waals spheres [4]. These representations generally take every particle constituting the molecules into account which gives a very accurate picture of the molecular systems. ...
... The algorithm has guarantees on topological equivalence and triangle quality but is generally unsatisfying on efficiency and robustness. In 2008, Chavent et al. (2008Chavent et al. ( , 2011 reported a fast rendering method of skin surface by utilizing GPU on the rendering phase. Lindow et al. (2010) speeded up the computation phases by computing the Voronoi structure in parallel, where only the relevant local neighborhoods were taken into consideration. ...
This study proposes a novel approach, namely, skin flow complex algorithm (SFCA), to decompose the molecular skin surface into topological disks. The main contributions of SFCA include providing a simple decomposition and fast calculation of the molecular skin surface. Unlike most existing works which partition the molecular skin surface into sphere and hyperboloid patches, SFCA partitions the molecular skin surface into triangular quadratic patches and rectangular quadratic patches. Each quadratic patch is proven to be a topological disk and rendered by a rational Bézier patch. The skin surface is constructed by assembling all rational Bézier patches. Experimental results show that the SFCA is more efficient than most existing algorithms, and produces a triangulation of molecular skin surface which is decomposable, deformable, smooth, watertight and feature-preserved.
... Various television companies were keeping pace with this emerging technology while introducing their 3D products to their traditional markets [2]. In the consumer electronics business, Nvidia 3D Vision was used in more than 500 video games [3], scientific visualization projects [4], technical workshops [5], and health therapy devices [6]. Even two out of twelve Hollywood's box office movies in 2009-2010 were delivered in both 2D and stereoscopic 3D format [7]. ...
The simulator sickness questionnaire (SSQ) has been a prevalent method to observe motion sickness in stereoscopic 3D motion picture. However, previous works do not provide adequate comprehension of the relationship between SSQ, depth gaze behavior, and heart rate variability in the stereoscopic 3D motion picture. To fill this research gap, we present a novel investigation of motion sickness in stereoscopic 3D movies using SSQ, electrocardiography (ECG), and 3D gaze tracking. Forty participants (N=40) watched only one of two 3D contents—3D content with a strong or a moderate sensation of vection. We observed that viewers of the 3D content with an intense feeling of vection more frequently reported symptoms of nausea (p<0.005) and disorientation (p<0.05) than their counterpart. SSQ, ECG, and 3D gaze tracking data show that sickness level could be reduced by persistently gazing at a particular point during exposure of 3D contents (p <0.001). Additionally, we found that individuals who were prone to motion sickness experienced depth gaze oscillation during several provoking scenes in dynamic 3D contents. Our experimental results may be used as a guideline in the development of a motion sickness predictor for various stereoscopic 3D motion pictures.
... We-bGL has built-in JavaScript APIs to render computer graphics by using graphics processing unit (GPU) acceleration. These features make WebGL an ideal substitute for Java Applet or Flash for 3D presentation (7). GLmol (http://www.glmol.com/), ...
A growing number of web-based databases and tools for protein research are being developed. There is now a widespread need for visualization tools to present the three-dimensional (3D) structure of proteins in web browsers. Here, we introduce our 3D modeling program-Web3DMol-a web application focusing on protein structure visualization in modern web browsers. Users submit a PDB identification code or select a PDB archive from their local disk, and Web3DMol will display and allow interactive manipulation of the 3D structure. Featured functions, such as sequence plot, fragment segmentation, measure tool and meta-information display, are offered for users to gain a better understanding of protein structure. Easy-to-use APIs are available for developers to reuse and extend Web3DMol. Web3DMol can be freely accessed at http://web3dmol.duapp.com/, and the source code is distributed under the MIT license.
