Single Particle Reconstruction with EMAN GroEL Methods for High - - PDF document

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Oct 09, 2023 134 likes •291 views

Single Particle Reconstruction with EMAN GroEL Methods for High Resolution Refinement Donghua Chen in Single Particle Processing Joanita Jakana Wah Chiu Steve Ludtke Jiu-Li Song (UT-SW Med) David Chuang (UT-SW Med) Asst. Professor,

SLIDE 1

Methods for High Resolution Refinement in Single Particle Processing

Steve Ludtke

Asst. Professor, Biochemistry, BCM

Co-director,NCMI

NCRR

Single Particle Reconstruction with EMAN GroEL

Donghua Chen Joanita Jakana Wah Chiu Jiu-Li Song (UT-SW Med) David Chuang (UT-SW Med)

EMAN: http://ncmi.bcm.tmc.edu/eman

GroEL 2000 (15 Å)

5000 particles, JEOL 4000

GroEL 2001 (11.5 Å)

5000 particles, JEOL 4000

GroEL 2003 (6 Å)

30,000 particles, JEOL 2010F

2005

SLIDE 2

Jeol 3000 7 Days of imaging, 910 micrographs 1.06 Å/pix, Nikon 9000 scanner 135 used, 34,868 particles Animation Unavailable in PDF version Animation Unavailable in PDF version Animation Unavailable in PDF version Animation Unavailable in PDF version Animation Unavailable in PDF version

SLIDE 3

Animation Unavailable in PDF version Animation Unavailable in PDF version Animation Unavailable in PDF version Animation Unavailable in PDF version Animation Unavailable in PDF version Animation Unavailable in PDF version

SLIDE 4

Animation Unavailable in PDF version Animation Unavailable in PDF version Animation Unavailable in PDF version

Ca2+ Release Channel

Irina Serysheva Wah Chiu Susan Hamilton

Myofibril Plasma membrane T-tubule Terminal cisternae of SR Tubules of sarcoplasmic reticulum

SR membrane, triggered by

DHPR in T-tubule

Homotetramer
~2200 kDa
Releases Ca++ which initiates

cross-bridge cycle

Ca2+ Release Channel

1.2 µm

200 kV image of ice-embedded RyR1 (no continuous CF)

500 Å

SLIDE 5

~30 Å Resolution

Animation Unavailable in PDF version

20 Å Resolution 14 Å Resolution 9.6 Å Resolution

Animation Unavailable in PDF version

SLIDE 6

270 Å 190 Å SR lumenal face

SR lumen Cytoplasm SR membrane T-tubule membrane

cytoplasmic face

}TM

Animation Unavailable in PDF version

Sequence assignment of observed helices

4864 –NKSEDEDEPDMKCDDMMTCYLFHMYVGVRAGGGIGDEIEDPAGDEYELYRVVFDITFFFFVIVILLAIIQGLIIDAFGELRDQQEQVKEDMETK- 4957

Filter M9 M10

RyR1:

Helix 2 Pore helix Inner helix

45 -SWTVSLYWTFVTIATVGYGDYSPSTPLGMYFTVTLIVLGIGTFAVAVERLLEFLINREQ- 103

Filter Hinge

MthK: KcsA:

36 -QLITYPRALWWSVETATTVGYGDLYPVTLWGRCVAVVVMVAGITSFGLVTAALATWFVGREQ -119

Filter Pore helix Inner helix Helix 2 Helix 1

RyR1

Pore helix Inner helix Filter

KcsA

Hinge Helix 1

MthK

Pore helix Inner helix Filter

kin k

Sequence assignment of observed helices

RyR1/KcsA/MthK kink

lumenal side (‘out’)

4864 –NKSEDEDEPDMKCDDMMTCYLFHMYVGVRAGGGIGDEIEDPAGDEYELYRVVFDITFFFFVIVILLAIIQGLIIDAFGELRDQQEQVKEDMETK- 4957

Filter M9 M10

RyR1:

Hinge

CCD Film + Scanner

CCD vs. Film

Initial 3D Model Particle Images Uniform Projections Build New 3-D Model Align and Average Classes Classify Particles Final 3D Model

SLIDE 7

Initial 3D Model Particle Images Uniform Projections Build New 3-D Model Align and Average Classes Classify Particles Final 3D Model Initial 3D Model Particle Images Uniform Projections Build New 3-D Model Align and Average Classes Classify Particles Final 3D Model Initial 3D Model Particle Images Uniform Projections Build New 3-D Model Align and Average Classes Classify Particles Final 3D Model Initial 3D Model Particle Images Uniform Projections Build New 3-D Model Align and Average Classes Classify Particles Final 3D Model Initial 3D Model Particle Images Uniform Projections Build New 3-D Model Align and Average Classes Classify Particles Final 3D Model

Refine from Gaussian Ellipsoid

SLIDE 8

Iteration 1 Iteration 2 Iteration 3 Iteration 4 Iteration 5 How do we get to Higher Resolutions?

Get a better microscope
Find a better microscopist
Algorithm Improvements

SLIDE 9

M(s) = F(s) C(s)2 E(s)2 + N(s)2 N(s) C(s) E(s) + N(s) M(s)2 F(s) C(s)2 E(s)2 + N(s)2

SLIDE 10

Image Classification

? ?

Initial 3D Model Particle Images Uniform Projections Build New 3-D Model Align and Average Classes Classify Particles Final 3D Model

Alignment/Registration

SLIDE 11

Alignment/Registration Alignment/Registration Alignment/Registration

Initial 3D Model Particle Images Uniform Projections Build New 3-D Model Align and Average Classes Classify Particles Final 3D Model

Measures of Similarity

Correlation Coefficient
Variance (transformed density)
Variance (matched filter)
Phase Residual
Mutual Information
etc.

(

)2 =

SLIDE 12

(

)2 =

(

)2 =

(

)2 =

And the Answer is…

Wiener filter particle
Filter reference to match
Normalize reference density to particle
Calculate variance

Initial 3D Model Particle Images Uniform Projections Build New 3-D Model Align and Average Classes Classify Particles Final 3D Model

Model Bias

25 100 250 1000 2000 Align to Noisy Base

SLIDE 13

Model Bias

25 100 250 1000 2000 Align to Noisy (~10% contrast) Base

Model Bias

25 100 250 1000 2000 Align to Base Noisy (~10% contrast)

Model Bias

25 100 250 1000 2000 Align to Noisy Base

Model Bias

25 100 250 1000 2000 Align to Noisy Base Iter x4

Model Bias

25 100 250 1000 2000 Align to Noisy Base Iter x8

Model Bias

25 100 250 1000 2000 Align to Base Noisy (~10% contrast)

SLIDE 14

Model Bias

25 100 250 1000 2000 Align to Noisy Base Iter x4

Initial 3D Model Particle Images Uniform Projections Build New 3-D Model Align and Average Classes Multi-Classify Particles Final 3D Model

Each particle -> best n classes
More restrictive exclusion from class-avg

The Future

Better similarity criteria
Improved CTF model
Per-particle CTF (at least defocus)
Beam tilt
Better 3-D reconstruction
New refinement methodologies