1. CCV, ICES, University of Texas at Austin Biomoleculer Visualization and Computations at CCV Angstrom Vinay K Siddavanahalli Center for Computational Visualization Institute of Computational and Engineering Sciences Department of Computer Sciences, University of Texas at Austin Ribosome, ribbon imposter rendering

2. CCV, ICES, University of Texas at Austin Application domain Visualization –Volume, Imposter and Isosurface models –Grid / client server rover based. –Compression based, and hardware accelerated algorithms Animation –Flexible models –Volumetric video compression and interactive rendering Bioinformatics –Quantitative –Qualitative –Topological Protein docking –Compressed format, with flexibility information

3. CCV, ICES, University of Texas at Austin Model creation – from the PDB database PDB files Electron density Electrostatic potential SES, SCS Volume + isocontours Volume + isocontours Linear, higher order meshes Imposter rendering Volume rendering of Rice dwarf virus Volume rendering of hemoglobin

4. CCV, ICES, University of Texas at Austin 2D Image Processing Reconstruction 3D Image Processing/Modelin g Particle Picking Classification Cryo-EM Images Particle Images Estimated Orientations Alignment & Averaging Alignment & Averaging Groups of Particles 3D Electron Density Map Refinement Adaptive Contrast Enhancement Adaptive Contrast Enhancement AdaptiveFiltering 2D/3D Image Enhancement and Correction CTF Correction 3D Image Segmentation 3D Image Segmentation Asymmetric Units Medial Axis Extraction Medial Axis Extraction Helices/Sheets Detection Helices/Sheets Detection Shape Matching Feature Extraction Feature Extraction Secondary Structures Pseudo - atomic Structures Gaussian Blurring Gaussian Blurring Protein Data Bank with other information Orientation Determination Orientation Determination Particle Averages Reconstruction from 2D to 3D Reconstruction from 2D to 3D Model creation – from imaging datasets

5. CCV, ICES, University of Texas at Austin Data structure We use a combined hierarchical Volumetric, Surface and Bond-level structural representation. Compressed data is used for time varying volume rendering and storage. We are also working on using it for other visualization algorithms including isosurface extraction. There are two distinct pipelines we follow to produce our datasets –From the PDB. ( from which we receive bond level information ) –Imaging data sets of large biomolecules.

6. CCV, ICES, University of Texas at Austin Protein specific data structure Groups of proteins Protein a Chain 1 Protein p Secondary structure 1Secondary structure s Residue 1 Chain c Residue r Atom list Since we use a hierarchical data structure for the bond-level domain, proteins can be represented naturally. Bond information, like connectivity and torsion angles along the backbone are also maintained for flexibility modeling and visualization Level of detail function computations and rendering is facilitated in this model. It is extensible; level can be added, removed easily and each level uses arrays than lists to enable fast array rendering. Each level is the same data structure, could just subclass to add more to it.

7. CCV, ICES, University of Texas at Austin Multiresolution images Hemoglobin Residues Secondary structures Backbone chains

8. CCV, ICES, University of Texas at Austin Volumetric visualization Volumes are generated either through Gaussian blurring ( to produce density maps ) or through APBS to obtain electrostatic potential maps. –Use texture based hardware rendering. –A hierarchical data structure on the bond level allows us to generate a multiresolution model of the volumetric fields. The multiresolution format is useful for level of detail rendering and adaptive protein docking. The volume data structure we use is a RAWV format. It is a header which contains a description of the data set, followed by the grid positioned voxel vector values. Internal structure is a 3d grid and a colormap structure.

9. CCV, ICES, University of Texas at Austin Internal Data Storage, Access DataManager has different DataSet Arrays Each dataType is associated with API, renderer, widgets The DataManager has a generic API with calls including load, delete, render etc. The DataSet implements general IO functions, including capabilities, presence of expected properties etc.

10. CCV, ICES, University of Texas at Austin Bond level rendering Large surface rendering can be prohibitive for interactive rendering. We use an imposter based model to render the ball and stick model. Only one rectangle per primitive ( like sphere or cylinder ) is required. Depth and normal mapping yields true high quality surfaces. Further speed up is achieved through our hierarchical model representation. Interactive rendering of the 1.2 million atom microtubule using the imposter model on PCs with NVIDIA programmable graphics cards

11. CCV, ICES, University of Texas at Austin Mesh generation Adaptive Volume Meshes are required for obtaining adaptive potential fields. Here, a simple listing of primitives is used as the file format rather than vrml or stl etc. Internally, surface meshes are stored and handled as isosurfaces 94847 vertices and 497327 tetrahedrons The active site groove is inside the red box. Adaptive meshes are generated in order to keep the accuracy of the groove, and reduce the number of elements at the same time. AcetylCholinesterase (257 3 )

12. CCV, ICES, University of Texas at Austin Flexibility modeling Bond angles representation for hierarchical modeling of flexibility. Volumetric video compression scheme for interactive rendering of 3d time varying data Time varying volumetric video Showing the hemoglobin action. Data by Dr.David Goodsell

13. CCV, ICES, University of Texas at Austin Compression based Computational Visualization We use compression for the following: –Storing, streaming large datasets, including isocontours and volumes and time varying volumes. –Represent functions of proteins in a hierarchical manner to: Render interactively and use Level of Detail algorithms Perform protein docking

14. CCV, ICES, University of Texas at Austin Linear Hierarchal Basis TC:571 Haar Wavelets TC:571

15. CCV, ICES, University of Texas at Austin

16. CCV, ICES, University of Texas at Austin Interrogative Visualization Query with a PDB file for additional information –Potential fields –Curvature calculations –Topological information –Fast isosurface mesh extraction Quantitative information –We have developed the contour spectrum, which we can use to obtain quantitative information like volume, surface and gradient information. –This supplements visualization for our understanding of the data sets Time varying volumes –Track time varying quantitative changes, like volumes of components. This helps to understand the change in properties of the biomolecule as it changes over time. Mean curvature of 1a06

17. CCV, ICES, University of Texas at Austin APIs Many libraries like isocontouring, volume rendering are easy to interface to. ( inputs, outputs easy to define, understand ) Imposter based rendering uses slightly different information format, but very similar to the hierarchical GroupOfAtoms data structure. Volume, topological, quantitative queries can be made again as calls to libraries.

18. CCV, ICES, University of Texas at Austin Resources CCV software can be downloaded from http://ccvweb.csres.utexas.edu We are recently working on grid enabled scientific visualization. –Collaborators include Steve Cutchin (SDSC), Erik Engquist (SDSC), Art Olson (TSRI), Michel Sanner (TSRI)

19. CCV, ICES, University of Texas at Austin Acknowledgements CCV –Dr C Bajaj –Julio Castrillon –Peter Djeu –SK Vinay –Zeyun Yu –Bong-Soo Sohn –Young-In Shin –Sangmin Park –Yongjie (Jessica) Zhang –Greg Johnson –Zaiqing Xu –KL Chandrasekhar –Qiu Wu –Jasun Sun –Anthony Thane –Shashank Khandelwal Computational resources –CCV/ICES/UT –NPACI/SDSC Sponsors –NSF –UT/MDACC/Whitaker –NPACI/NSF –DOE-LLNL/Sandia