molecular surface abstraction
DESCRIPTION
Molecular Surface Abstraction. Gregory Cipriano and Michael Gleicher University of Wisconsin-Madison. Structural Biology: form influences function. Standard metaphor: Lock and key Proteins and their ligands have complementary Shape Charge Hydrophobicity . The functional surface. - PowerPoint PPT PresentationTRANSCRIPT
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Molecular Surface Abstraction
Gregory Cipriano and Michael GleicherUniversity of Wisconsin-Madison
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Structural Biology: form influences function
Standard metaphor: Lock and key
Proteins and their ligands have complementary• Shape• Charge• Hydrophobicity• ...
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Solvent excluded interface
The functional surface
Porin Protein (2POR)
Charge fields
Binding partners
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Hard to get the big picture…The surface is just too complex.
How scientists currently look at molecular surfaces
Porin Protein (2POR)
See the hole?
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Our surface abstraction
Porin Protein (2POR)
Guiding principle:
Convey the big picture, without getting mired in detail…
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Our surface abstraction
Ligands were here
Hole through protein is now visible
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Our source data:The geometric surface
A (naked) geometric surface
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Our source data:The geometric surface
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Our source data:The charge field
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Another confusing surface
Catalytic Antibody (1F3D)Rendered with PyMol
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Prior art: QuteMol
Stylized shading helps convey shape
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How do molecular biologists deal with visual complexity?
Abstracted ribbon representation.
Confusing stick-and-ball model
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How do they do the same thing with surfaces?
... they don't.
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Our method: abstraction
Abstracts both geometry and surface fields (e.g. charge).
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But wait! There’s more...
We show additional information using decals.
Why? We have more to show, and we’re already using color.
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How we can use decals
Predicted LigandBinding Sites
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How we can use decals
Ligand Shadows
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Abstraction in 4 steps
Our method:
1. Diffuse surface fields2. Smooth mesh3. Identify and remove remaining high-curvature regions4. Build surface patches and apply a decal for each patch
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Abstraction in 4 steps
Our method:
1. Diffuse surface fields2. Smooth mesh3. Identify and remove remaining high-curvature regions4. Build surface patches and apply a decal for each patch
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Diffusing surface fields
Starting with a triangulated surface:
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Diffusing surface fields
Starting with a triangulated surface:
We sample scalar fieldsonto each vertex:
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Diffusing surface fields
We sample scalar fieldsonto each vertex:
And smooth them, preservinglarge regions of uniform value.
Starting with a triangulated surface:
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Smoothing
Standard Gaussian smoothing tends to destroy region boundaries:
Weights pixel neighbors by distance when averaging.
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Bilateral filtering
A bilateral filter* smooths an image by taking into account both distance and value difference when averaging neighboring pixels.
* C. Tomasi and R.Manduchi. Bilateral filtering for gray and color images. In ICCV, pages 839–846, 1998.
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Bilateral filtering
A bilateral filter* smooths an image by taking into account both distance and value difference when averaging neighboring pixels.
...producing a smooth result while still retaining sharp edges.
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Bilateral filtering
We do the same thing, but on a mesh:
A vertex and its immediate neighbors
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Abstraction in 4 steps
Our method:
1. Diffuse surface fields2. Smooth mesh3. Identify and remove remaining high-curvature regions4. Build surface patches and apply a decal for each patch
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Smoothing the mesh
Taubin* (lambda/mu) smoothing
Pros:• Fast• Volume preserving• Easy to implement
* G. Taubin. A signal processing approach to fair surface design. In Proceedings of SIGGRAPH 95, pages 351–358.
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The trouble with smoothing...
Taubin (lambda/mu) smoothing
Cons:• Contractions produce artifacts• Resulting mesh still has
regions of high curvature...
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Abstraction in 4 steps
Our method:
1. Diffuse surface fields2. Smooth mesh3. Identify and remove remaining high-curvature regions4. Build surface patches and apply a decal for each patch
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Further abstraction: “sanding”
Select a user-defined percentageof vertices with highest curvature.
Grow region about each point.
Remove, by edge-contraction, allbut a few vertices in each region, proceeding from center outward.
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Final smooth mesh
Original Completely smooth With Decals
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Abstraction in 4 steps
Our method:
1. Diffuse surface fields2. Smooth mesh3. Identify and remove remaining high-curvature regions4. Build surface patches and apply a decal for each patch
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Maps a piece of the surface to a plane
Parameterization
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We parameterize the surface with Discrete Exponential Maps*
Advantages:• Very fast
* R. Schmidt, C. Grimm, and B.Wyvill. Interactive decal compositing with discrete exponential maps. ACM Transactions on Graphics, 25(3):603–613, 2006.
Disadvantages:• Not optimal• Doesn’t work well for
large regions
Parameterization
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'H' stickers represent potential hydrogen-bonding sites
Decal type #1: Points of interest
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Decal type #2: Regions
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Decal type #2: Regions
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Decal type #2: Regions
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Decal type #2: Regions
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Surface patch smoothing
Before After
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Examples
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Examples
(1AI5)
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Examples
(Onconase)
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Examples
(1GLQ)
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Examples
(1ANK)
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Issues with our method
Where we fail:• Very large molecules need new abstractions• Parameterizing large regions• Possibly important fine detail lost• Lots of parameters• No real evaluative studies (yet)
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Conclusion
Molecular surface abstraction:• Preserves large-scale structure• Complements existing visualizations• Allows for quick assessment of complex surfaces
Thanks to: Michael Gleicher, George Phillips, Aaron Bryden, Nick Reiter. And to CIBM grant NLM-5T15LM007359
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Thank you!
Questions?