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Published byDestinee Osborne Modified about 1 year ago

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Icosahedral Reconstruction Wen Jiang

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Icosahedral Symmetry 5, 3, 2 fold axes –6 five fold axes –10 three fold axes –15 two fold axes 60 fold symmetry in total

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X Z Y X Y Z Conventions MRC X, Y, Z axes along 2 fold sym axes for orientation (0,0,0) Euler: Z Y’ Z’’ EMAN Z along 5 fold sym axis Y along 2 fold sym axis X near 3 fold sym axes for orientation (0,0,0) Euler: Z X’ Z’’

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pyruvate dehydrogenase 200Å P22 phage 700Å Herpes Simplex Virus 1250Å Icosahedral Particles

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Image Processing Preprocessing Particle selection CTF fitting Image refinement Orientation determination 3-D reconstruction

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Particle Selection By Kivioja and Bamford et al. “Ring Filter” Only ONE parameter (R) Fast, seconds / micrograph Tend to over-select Manual selection boxer Automated selection batchboxer ethan (for spherical particles)

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Herpes 2.1 Å/pxiel 6662x8457 pixels 15 seconds in total $ time ethan.py jj0444-8bit.mrc jj0444.box jj0444.img Opening file jj0444-8bit.mrc Width: 6662 Height: 8457 Averaging 121 pixels Filtering jj0444-8bit.mrc: ********** Total number of squares is 567 Number of peaks after sector test is 163 Number of peaks after first distance check is 81 Number of peaks after discarding too small ones is 80 Refining centers 74 particles found from jj0444-8bit.mrc 10.31s user 2.08s system 84% cpu total Ethan Example

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CTF Fitting Manual fitting ctfit Automated fitting fitctf fitctf.py subject to: Algorithm: constrained nonlinear optimization using a sequential quadratic programming (SQP) method) 8 unknown parameters: Z, B, A, Q, n1, n2, n3, n4

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Z= -1.14μm Z= -0.41μm RDV Z= -3.35μm Z= -2.11μm RyR1 fitctf.py Examples

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Image Refinement Classic method common-lines Fourier-Bessel reconstruction EMAN method projection matching direction Fourier inversion reconstruction

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Icosahedral Reconstruction Using EMAN EMAN now supports icosahedral reconstruction User interface is essentially the same as for other symmetries A few points special for large virus aimed at high resolution: projections at 1GB memory, add “projbatches= ” with n>1 to refine half 3-D map can be used for large 3-D map (>500 3 ) 3-D reconstruction use only single CPU. can take several hours to reconstruction a large 3-D map. working on parallel version support mixed platform processing

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Cross Common Line From Z. Hong Zhou’s dissertation, p36 Intersection of two planes in Fourier space

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ref raw Orientation Common Line Locations common line in 3D: Fourier plane normal in 3D: common line in 2D:

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Orientation Determination By Common Line Search search for the orientation and center parameters that minimize the mean phase differences among all the pairs of common lines

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Search Strategy Original Fortran implementation (Crowther et al) center the particle by cross-correlation self common line to get a starting orientation by exhaustive searching orientation in 1° step cross common line to do local refinement of orientation and center by Gradient or Simplex.

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Search Strategy Exhaustive cross common line search enumerate all centers and orientations in an asymmetric unit simple but really slow for 1 degree, 1 pixel step: 3X10 7 trials/particle

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Search Strategy A new global optimization algorithm hybrid Monte Carlo and Simplex global search for both orientation and center complicated, but accurate and fast ~4x10 3 trials/particle. >10 4 times faster than exhaustive search

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Is The Best Solution Correct? Absolute score (residual) cutoff dominant criterion needs different cutoff values for images at different defocuses how to pick a single number that suits all imaging conditions?

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Z score cutoff Is The Best Solution Correct? 3.5 residual μ safe range x better, less sensitive to defocus unstable for very small defocus (<1μm)

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Is The Best Solution Correct? Consensus cutoff agreement among different methods (projection matching vs. common line) agreement among multiple runs of the global common line search more reliable than absolute value and Z- score cutoff bias toward “safer side”

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Fourier Bessel 3-D Reconstruction SXSX SySy SzSz Very efficient Parallel reconstruction

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I: IMIRS ortAll, eliminateOrt, buildTemplate, globalRefine, ctfForAll, refineAll, reconstructParallel II: EMAN C++/Python programming library, runpar, proc2d, proc3d, volume, iminfo, ctfit, boxer III: New C++ algorithmic programs WaveOrt, cenalign, symmetrize, icosmask, projecticos IV: New C++ glue programs fftimagic, Imagic2OrtCen, OrtCen2Imagic V: New Python scripts Class Module : EM.py Program : virus.py, crossCommonLineSearch.py SAVR: Semi-Automated Virus Reconstruction

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CTF fitting Refine center / orientation Initial model Reconstruction Model re- projection Initial center / orientation

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Features of SAVR Minimal user inputs to start processing and user intervention free once started Cross platform compatible (shared memory and distributed memory systems) All computation intensive steps parallelized Free from NCMI Web server (http://ncmi.bcm.tmc.edu) Fast crash recovery

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P22 Procapsid (8.5Å)P22 Phage (9.5Å) Herpes (8.5Å)Rotavirus (9Å) Rice Dwarf (6.8Å) Virus Structures At Sub-nanometer Resolutions Using SAVR CPV (9Å, 6Å)ε 15 Phage (9Å)

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