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Icosahedral Reconstruction Wen Jiang. Icosahedral Symmetry 5, 3, 2 fold axes –6 five fold axes –10 three fold axes –15 two fold axes 60 fold symmetry

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  • Icosahedral ReconstructionWen Jiang

  • Icosahedral Symmetry5, 3, 2 fold axes6 five fold axes10 three fold axes15 two fold axes60 fold symmetry in total

  • ConventionsXZYMRCX, Y, Z axes along 2 fold sym axes for orientation (0,0,0)Euler: Z Y ZEMANZ along 5 fold sym axisY along 2 fold sym axisX near 3 fold sym axes for orientation (0,0,0)Euler: Z X Z

  • Icosahedral Particlespyruvate dehydrogenase200 P22 phage 700Herpes Simplex Virus1250

  • Image ProcessingPreprocessingParticle selectionCTF fittingImage refinementOrientation determination3-D reconstruction

  • Particle SelectionBy Kivioja and Bamford et al.Ring FilterOnly ONE parameter (R) Fast, 10-15 seconds / micrographTend to over-selectManual selectionboxerAutomated selectionbatchboxerethan (for spherical particles)

  • Ethan ExampleHerpes 2.1 /pxiel 6662x8457 pixels 15 seconds in total$ time ethan.py jj0444-8bit.mrc 298 600 jj0444.box jj0444.imgOpening file jj0444-8bit.mrcWidth: 6662Height: 8457Averaging 121 pixelsFiltering jj0444-8bit.mrc: **********Total number of squares is 567Number of peaks after sector test is 163Number of peaks after first distance check is 81Number of peaks after discarding too small ones is 80Refining centers74 particles found from jj0444-8bit.mrc

    10.31s user 2.08s system 84% cpu 14.578 total

  • CTF FittingManual fittingctfitAutomated fittingfitctffitctf.py

  • fitctf.py Exampleshttp://ncmi.bcm.tmc.edu/homes/wen/fitctf

  • Image RefinementClassic methodcommon-linesFourier-Bessel reconstructionEMAN methodprojection matchingdirection Fourier inversion reconstruction

  • Icosahedral Reconstruction Using EMANEMAN now supports icosahedral reconstructionUser interface is essentially the same as for other symmetriesA few points special for large virus aimed at high resolution:projections at 1GB memory, add projbatches= with n>1 to refinehalf 3-D map can be used for large 3-D map (>5003)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

  • Cross Common LineFrom Z. Hong Zhous dissertation, p36Intersection of two planes in Fourier space

  • Orientation Common Line LocationsrefrawFourier plane normal in 3D:

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

  • Search StrategyOriginal Fortran implementation (Crowther et al)center the particle by cross-correlationself common line to get a starting orientation by exhaustive searching orientation in 1 stepcross common line to do local refinement of orientation and center by Gradient or Simplex.

  • Search StrategyExhaustive cross common line searchenumerate all centers and orientations in an asymmetric unitsimple but really slowfor 1 degree, 1 pixel step: 3X107 trials/particle

  • Search StrategyA new global optimization algorithmhybrid Monte Carlo and Simplexglobal search for both orientation and centercomplicated, but accurate and fast ~4x103 trials/particle. >104 times faster than exhaustive search

  • Is The Best Solution Correct?Absolute score (residual) cutoffdominant criterionneeds different cutoff values for images at different defocuseshow to pick a single number that suits all imaging conditions?

  • Is The Best Solution Correct?Z score cutoffbetter, less sensitive to defocusunstable for very small defocus (
  • Is The Best Solution Correct?Consensus cutoffagreement among different methods (projection matching vs. common line)agreement among multiple runs of the global common line searchmore reliable than absolute value and Z-score cutoffbias toward safer side

  • Fourier Bessel 3-D ReconstructionVery efficientParallel reconstruction

  • III: New C++ algorithmic programs WaveOrt, cenalign, symmetrize, icosmask, projecticosIV: New C++ glue programs fftimagic, Imagic2OrtCen, OrtCen2ImagicSAVR: Semi-Automated Virus Reconstruction

  • SAVR: Semi-Automated Virus ReconstructionCTF fittingRefine center / orientationInitial modelReconstructionModel re-projection

    Initial center / orientation

  • Features of SAVRMinimal user inputs to start processing and user intervention free once startedCross 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

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

    Kivioja T, Ravantti J, Verkhovsky A, Ukkonen E, Bamford D. Local Average Intensity-Based Method for Identifying Spherical Particles in Electron Micrographs. Journal of Structural Biology Vol. 131, No. 2, pp. 126-134, August 1, 2000784*360*10*10 =2.82X107 trials/particle