Difficult Problems & EMAN2 Introduction

Transcription

1 Difficult Problems & EMAN2 Introduction Steve Ludtke National Center for Macromolecular Imaging Biochemistry & Molecular Biology Baylor College of Medicine

2 Initial Model Bias? EMAN2 'monte carlo' approach: Random starting model, filtered, masked Rapid refinement against ref free classes Repeat many times, sort results in quality order

3 Can you trust it? Very reliable, EXCEPT: Particles with a lot of motion/heterogeneity Particles with strong preferred orientation Particles with insufficient contrast (bad data) If you don't know the quaternary structure, you should ALWAYS use tilt-validation. Ribosomes refine from anything GroEL actually one of the hardest cases

4 3-D Reconstruction Iterative (automated) process Start with particles and an initial guess at the 3-D structure The guess need not be very good

5 GroEL ~800 kda homo 14-mer Type 1 chaperonin (GroES co-chaperonin) Several crystal structures available

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

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

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

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

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

11 Refine from Gaussian Ellipsoid

12 Iteration 1

13 Iteration 2

14 Iteration 3

15 Iteration 4

16 Iteration 5

17 Do Initial Models Matter?

18 Do Initial Models Matter? No, not really

19 Do Initial Models Matter? No, not really... as long as you validate

20

21 Difficult Problems

22 Difficult Problems? Icosahedral virus? Large -> a lot of RAM High symmetry few particles, faster Conclusion: easy Caveat: Assuming it doesn't move

23 Difficult Problems? Icosahedral virus, no symmetry (portals) Large a lot of RAM 60x more particles for same resolution 60x more projections with no symmetry 3600x more computation! Conclusion: easy, but need big computer Caveat: portal symmetry mismatch/stability

24 Difficult Problems? Asymmetric particle (eg - ribosome) No symmetry more particles & computation Initial model bias No Motion depends on scope of motion Conclusion: easy, but may need big computer Caveat: too much motion may be trouble

25 Difficult Problems? Small Objects (50 kda ribozyme fragment) Contrast can be too low to find/align particles Conclusion: hard. Phase plates to the rescue!

26 Difficult Problems? Pseudosymmetry (TRiC) 8 highly homologous subunits Sufficient resolution in each particle needed to break symmetry How to validate? Conclusion: Hard! Caveat: easy if you impose symmetry

27 Difficult Problems? Structural Heterogeneity

28 Difficult Problems? Structural Heterogeneity

29 Difficult Problems? Large Structural Heterogeneity Hard to even get relative orientations of different views. May be impossible without tilt. Caveat: New computational approaches may help.

30 Difficult Problems? Strongly Preferred Orientation May be impossible without tilting. May be impossible to get a good structure even with tilting. Solution generally in specimen prep, not computation.

31 Difficult Problems? Discrete heterogeneity (fragile complexes, ligand binding) Requires n times more particles to cover states Population distribution in different states important. Various multi-model refinement strategies Conclusion: can be quite easy Caveat: hard to generalize, depends on system

32 Multireference Refinement 3D Model Projections 3D Model 3D Model Projections 3D Model 3D Model Projections 3D Model Particles Classify Particles Class Avg. Class Avg. Class Avg.

33 How long will my refinement take? Relax n-fold symmetry n 2 x compution, n x data Double resolution 2 x sampling, 4 x frames, typ 10 x more particles (if previously res limited), computation x increase Look at a particle 2x larger similar to doubling resolution n-fold multi-reference refine n x particles, n x references, so ~n 2 more computation

34 EMAN2 Introduction

35 Why EMAN2.1(alpha) BDB: is gone!!! New e2refine_easy Implements gold-standard Maps auto-filtered Faster! Requires only a few parameters Improved GUI tools

36 EMAN2 EMAN2 Wiki: Software Download page: software_details?selected_software=counter_222 Discussion Mailing List/Google Group: forum/eman2

37 EMAN2 Architecture Ease of Use Project Manager Interface High-Level Programs Command-Line Programs Python Core Flexibility C++ Core

38 Complete graphical workflow Project system which organizes data and records all reconstruction info. Qt/OpenGL for 2d & 3d display. Refinements ~5-20x faster than EMAN1 Support for all documented cryoem file formats. Over 200 image processing algorithms Use EMAN2 to launch Frealign & Relion refinements Tilt Validation, Random Conical Tilt, Single Particle Tomography Parallel processing via 3 different mechanisms Improved CTF correction, cont C-film, energy filter

39 File Formats MRC R/W IMAGIC R/W SPIDER R/W HDF5 R/W PIF R/W ICOS R/W VTK R/W PGM R/W Amira R/W Xplor W Gatan DM2 R Gatan DM3 R Gatan DM4 R FEI SER R TIFF R/W Scans-a-lot R LST R/W PNG R/W Video-4-Linux R JPEG W

40 Programs Command-Line Programs (EMAN2) syntax: e2<name>.py --help e2<name>.py <file> [--option=value] [--option] [-O] <> - required parameter [] - optional parameter e2help.py <category>

41 Utility Programs e2version.py - Display version info e2speedtest.py - Test machine performance e2help.py - Documentation for modular functions e2display.py - GUI for general visualization e2proc2d.py - 2d image processing of stacks and single images e2proc3d.py - 3d image processing of 3-D stacks and single volumes e2iminfo.py - general image information tool e2.py - Python command-line for EMAN2

42 GUI e2projectmanager.py - NEW workflow dialog (replaces e2workflow) e2display.py - General image/volume display e2boxer.py - Interactive particle picker e2helixboxer.py - Filament picker e2tomoboxer.py - Interactive tomogram picker e2ctf.py - Various CTF operations e2eulerxplor.py - Look at particle orientations e2simmxxplor.py - Evaluate how well orientations can be determined e2cmpxplor.py - Evaluate how different similarity metrics work

43 High Level Programs e2refine2d.py - reference free class-averages e2initialmodel.py - Make initial models from a few class-averages e2refine.py - Standard single particle analysis 3-D refinement e2eotest.py - even/odd test for resolution assessement e2refinevariance.py - Compute a variance map e2refinemulti.py - multiple map simultaneous refinement e2classifyligand.py - Split data into 2 groups based on 2 models e2refinetofrealign.py - Set up for a Frealign run based on an EMAN2 refinement e2runfrealign.py - Execute Frealign e2refinefromfrealign.py - Process the results of a Frealign run

44 Extensible Core Type Description # Processor Aligner Projector Reconstructor Cmp Averager Analyzer Orientgen Generic image processing algorithms, filters, masks, thresholds, etc. 180 Algorithms used to align 2 images or volumes to each other 22 Routines to generate 2-D projections of 3-D objects 6 Routines to reconstruct 3-D objects from 2-D projections 11 Similarity metrics used to compare two images or volumes 10 Average together stacks of images in various ways 7 Perform various operations on sets of images, such as classification or PCA 6 Routines describing how projections cover the asymmetric triangle 6

45 Processors (categories & examples) filter filter.lowpass.gauss filter.homomorphic.tophat mask mask.sharp mask.gaussian math math.sqrt math.laplacian misc misc.localnorm normalize normalize normalize.edgemean testimage testimage.scurve threshold threshold.binary threshold.clampminmax xform xform.centerofmass xform.fourierorigin.tocenter

46 Similarity Metrics (cmp) With Default options, SMALLER -> more similar dot - dot product (negative by default) frc - Fourier ring correlation (weighted) optvariance - optimized variance (EMAN1) phase - mean phase error quadmindot - Worst of quadrant dot products sqeuclidean - sum (a-b) 2 /n