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

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

CCV, ICES, University of Texas at Austin

Model creation – from the PDB database

PDB files

Electrondensity

Electrostaticpotential

SES, SCS

Volume + isocontours

Volume + isocontours

Linear, higher order meshes

Imposter rendering

Volume rendering of Rice dwarf virus

Volume rendering of hemoglobin

CCV, ICES, University of Texas at Austin

2D Image Processing

Reconstruction

3D Image Processing/Modeling

Particle PickingParticle Picking

ClassificationClassification

Cryo-EMImages

ParticleImages

EstimatedOrientations

Alignment

& Averaging Alignment

& Averaging

Groups ofParticles

3D ElectronDensity Map

Refi

nem

en

t

Adaptive ContrastEnhancement

Adaptive ContrastEnhancement

AdaptiveFilteringAdaptiveFiltering

2D/3D Image Enhancement and Correction

CTF CorrectionCTF Correction

3D Image Segmentation

3D Image Segmentation

Asymmetric Units

Medial Axis Extraction

Medial Axis Extraction

Helices/SheetsDetection

Helices/SheetsDetection Shape Matching Shape Matching

FeatureExtraction Feature

Extraction

SecondaryStructures

Pseudo-atomicStructure

s

Gaussian Blurring

Gaussian Blurring

ProteinData Bank

with other information

Orientation

Determination

Orientation

Determination

ParticleAverages

Reconstruction from 2D to 3D

Reconstruction from 2D to 3D

Model creation – from imaging datasets

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.

CCV, ICES, University of Texas at Austin

Protein specific data structureGroups of proteins

Protein a

Chain 1

Protein p

Secondary structure 1 Secondary 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.

CCV, ICES, University of Texas at Austin

Multiresolution images

HemoglobinResidues

Secondary structures

Backbone chains

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.

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.

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

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 (2573)

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 videoShowing the hemoglobin action.Data by Dr.David Goodsell

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

CCV, ICES, University of Texas at Austin

Linear Hierarchal BasisTC:571

Haar WaveletsTC:571

CCV, ICES, University of Texas at Austin

Rate Distortion (2EZP)

010203040506070

0.070.0

80.1

00.1

50.2

80.4

50.8

11.0

71.2

51.5

01.7

22.8

5

bits/voxel

PS

NR

(d

B)

Linear HBHarr

Rate Distortion (Hemoglobin)

0

10

20

30

40

50

60

0.08

0.10

0.14

0.20

0.29

0.45

1.06

1.72

2.30

2.85

3.00

bits/voxel

PS

NR

(d

B)

Linear HBHarr

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

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.

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)

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