Cryo-Electron Microscopy James Conway University of Pittsburgh School of Medicine 1. Making images image formation contrast function detectors – film & scanning, CCD, DED energy filters phase plates 2. Making density maps (AUTO3DEM) calculating 3D density maps from 2D projections estimating resolution modelling Penn State Med School – Friday, 4-Oct-2013

1. How does 3D reconstruction work anyway? Images are 2D projections of the 3D structure Simple approach is to back-project into a 3D volume More computationally efficient is to perform the equivalent operation in Fourier (reciprocal) space Easier to demonstrate back-projection using 1D projections from a 2D object Penn State Med School – Friday, 4-Oct-2013

Reconstructions - Central Section Theorem Projection onto 2D plane (microscopy) Fourier Transform Fit through center of 3D volume Fourier Transform (inverse) center of Fourier plane center of Fourier volume Reconstruction pathways Need many images at different orientations to fill Fourier volume or Real Space volume Raw images

Object Back-Projection in 2D - 1. Projection images

Object Back-Projection in 2D - 2. Projecting backwards

Object Back-Projection in 2D - 3. Finer sampling 10°5°1°30°

Object Back-Projection in 2D - 4. Details 10° 1° R-weighted

Object Back-Projection in 2D - 5. Another Example 1° R-weighted

Reconstructions - Central Section Theorem Projection onto 2D plane (microscopy) Fourier Transform Fit through center of 3D volume Fourier Transform (inverse) center of Fourier plane center of Fourier volume Reconstruction pathways Need many images at different orientations to fill Fourier volume or Real Space volume Raw images

Tim Baker Purdue/UCSD 2. Reconstruction Process a.Particle Picking b.CTF estimation c.RMC – initial model search d.Auto3Dem e.Resolution f.Interpretation & modelling Penn State Med School – Friday, 4-Oct-2013

2a. Particle Picking Penn State Med School – Friday, 4-Oct Å 1000Å

2a. Particle Picking Penn State Med School – Friday, 4-Oct-2013

2a. Particle Picking Penn State Med School – Friday, 4-Oct capsids21 capsids 451 x 451 pixels

2b. CTF Estimation Penn State Med School – Friday, 4-Oct-2013 micrograph

2b. CTF Estimation Penn State Med School – Friday, 4-Oct-2013 power spectrum

2b. CTF Estimation Penn State Med School – Friday, 4-Oct-2013 power spectrum

2a & b. Summary Penn State Med School – Friday, 4-Oct-2013 Picked 579 biggies & 21 smallies from 1 micrograph (2134) Repeat for other micrographs: ugraphBigSmalltotal Total Mean Defocus estimates ugraphdefocus Continue with analysis of biggies…

2c. RMC – initial model search Penn State Med School – Friday, 4-Oct-2013 Iterate search with new model for 10 rounds Repeat whole procedure several times with different starting orientations – look for acceptable and consistent results.

2c. RMC – initial model search Penn State Med School – Friday, 4-Oct-2013 conway% setup_rmc SETUP_RMC version v NAME setup_rmc - script for setting up random model calculation EXAMPLE setup_rmc -ncpu 4 -seed 123 -list listfile setup_rmc -usedefaults DESCRIPTION All input specified using the syntax -key value. Defaults values are shown in brackets following descriptions. To use all default values, use the -usedefaults flag. 'n' = integer, 'f' = float, 's' = string -boxrad n image box size [obtained from image file] -fsc_nbins n number of bins for FSC calculation [50] -fsc_res_min f minimum resolution (A) for FSC calculation [60] -fsc_res_max f maximum resolution (A) for FSC calculation [30] -list s file or expression listing particle parameter files If not specified, then setup_rmc looks for (1) file named 'list' or (2) *00n files in rundir -multi run multiple RMCs in parallel -ncpu n number of CPUs for each RMC computation[4] -nimages n number of images to use in constructing model [150] -nmodels n number of random models [10] -noctf turn off CTF corrections -nodefile s file containing list of compute nodes -res f highest resolution used in constructing map -rmax n maximum capsid radius -rmin n minimum capsid radius -rundir s directory containing particle parameter files [dat] -seed n seed for random number generator. Use an integer for reproducible result or -seed=time for seed based on seconds since 1/1/1970 [1] -symm_code n symmetry of reconstruction [532] -trad traditional RMC calculation (best map chosen using FSC)

2c. RMC – initial model search Penn State Med School – Friday, 4-Oct-2013 conway% setup_rmc –nmodels setup_rmc starting --- setup_rmc parameters: Default rundir = dat Default maximum map resolution = 29 list not specified - setup_rmc will first check for file 'list', then look for files dat/*00n Default ncpu = 4 Default fsc_nbins = 50 Default fsc_res_min = 60.0 Default fsc_res_max = 30.0 Default nimages = 150 User specified nmodels = 5 Default symm_code = 532 Default seed = 1 Parameter files in dat considered for model construction: all5.dat_000 Box radius from PIF/MRC file = 112 Instructions: Run RMC_run to construct starting model Run RMC_cleanup to remove temporary files after calculations complete Starting model will be found at dat/rmc.pif auto3dem input file auto-bin2_master automatically generated --- setup_rmc done --- conway% RMC_run Note – 150 particles only

2c. RMC – initial model search Penn State Med School – Friday, 4-Oct Trial 1 Iteration 0

2c. RMC – initial model search Penn State Med School – Friday, 4-Oct-2013 Trial 1Trial 2Trial 3Trial 4 Trial 5 “Best” Not precise, but accurate

2d. Auto3Dem Penn State Med School – Friday, 4-Oct-2013 i.Initial orientation search – we have a model (RMC) – but no orientations ii.Refinement – improve orientations – improve resolution global refine local refine automodesearch automoderefine Uses PPFT Uses POR po2rmodeglobal po2rmodelocal

2d. Auto3Dem Penn State Med School – Friday, 4-Oct-2013 PPFT POR

2d. Auto3Dem i. Initial orientation search (PPFT) Penn State Med School – Friday, 4-Oct-2013 vs. I usually run 10 iterations to ensure the model is decent and particle orientations have a chance to sample their correct values Use coarse angle steps – want a quick answer, rough but accurate e.g. ppftdelta_theta2 ppftbin_factor2

Projection from RMC map 2d. Auto3Dem i. Initial orientation search (PPFT) automodesearch... ppftannulus_high110 ppftannulus_low33 ppftbin_factor2 ppftctfmode3 ppftdelta_theta2 ppftfilter_factor_10.1 ppftinput_mode2 ppftpftrad_hi112 ppftpftrad_lo1 ppftpftrad_step1 ppftresolution_high30 ppftresolution_low124.2 ppfttemperature_factor0 Penn State Med School – Friday, 4-Oct-2013

2d. Auto3Dem i. Initial orientation search (PPFT) delta map map itr mode estres angle undamp damp time cpu nptles ntot nmg defocus # cycle 1. Start with RMC model (5), 5 iterations 1 s(2) s(2) s(2) s(2) s(2) Penn State Med School – Friday, 4-Oct-2013 RMCiter 1 iter 2 iter 3 iter 4 iter 5 150p2639p 2638p

2d. Auto3Dem i. Initial orientation search (PPFT) delta map map itr mode estres angle undamp damp time cpu nptles ntot nmg defocus # cycle 1. Start with RMC model (5), 5 iterations 1 s(2) s(2) s(2) s(2) s(2) Penn State Med School – Friday, 4-Oct iter 1 iter 2

2d. Auto3Dem i. Initial orientation search (PPFT) delta map map itr mode estres angle undamp damp time cpu nptles ntot nmg defocus # cycle 1. Start with RMC model (5), 5 iterations 1 s(2) s(2) s(2) s(2) s(2) Penn State Med School – Friday, 4-Oct

2d. Auto3Dem i. Initial orientation search (PPFT) Penn State Med School – Friday, 4-Oct-2013 RMCiter 5RMCiter 5

autoscore_fraction0.75 2d. Auto3Dem ii. Global orientation refine (POR mode global) Penn State Med School – Friday, 4-Oct-2013 Better model, starting orientations for all particles POR/global generally does a better job than PPFT But…very slow After PPFT, do 1 iteration of POR/global, coarse steps automoderefine... # Iteration parameters autoiter_start6 autoniter1... po2rctfmode2 po2rdangle2 po2rdcenter3 po2rgangle3 po2rmodeglobal po2rnangle4 po2rncenter4 po2rres_max12.65 po2rres_min124.2 po2rtempfac0 delta map map itr mode estres angle undamp damp time cpu nptles ntot nmg defocus # cycle 1. Start with RMC model (5), 5 iterations 1 s(2) s(2) s(2) s(2) s(2) # cycle 2. Global POR, 1 iteration 6 r(g)

2d. Auto3Dem ii. Global orientation refine (POR mode global) Penn State Med School – Friday, 4-Oct-2013 delta map map itr mode estres angle undamp damp time cpu nptles ntot nmg defocus # cycle 1. Start with RMC model (5), 5 iterations 1 s(2) s(2) s(2) s(2) s(2) # cycle 2. Global POR, 1 iteration 6 r(g) iter p iter p

2d. Auto3Dem ii. Global orientation refine (POR mode global) Penn State Med School – Friday, 4-Oct-2013 delta map map itr mode estres angle undamp damp time cpu nptles ntot nmg defocus # cycle 1. Start with RMC model (5), 5 iterations 1 s(2) s(2) s(2) s(2) s(2) # cycle 2. Global POR, 1 iteration 6 r(g) iter Å iter Å iter Å

2d. Auto3Dem ii. Global orientation refine (POR mode global) Penn State Med School – Friday, 4-Oct-2013 delta map map itr mode estres angle undamp damp time cpu nptles ntot nmg defocus # cycle 1. Start with RMC model (5), 5 iterations 1 s(2) s(2) s(2) s(2) s(2) # cycle 2. Global POR, 1 iteration 6 r(g) r(g) Iteration 6 repeated with a smaller angle (2° instead of 3°) No significant change in resolution Took 3 times longer to run (86 mins vs. 29 mins) Don’t make your angle or center steps too small!!

2d. Auto3Dem ii. Local orientation refine (POR mode local) Penn State Med School – Friday, 4-Oct-2013 Better model, better orientations POR/local is faster by inspecting a local window of orientations Do cycles of 5 iterations, reducing angle & center steps automoderefine... # Iteration parameters autoiter_start7 autoniter5... po2rctfmode2 po2rdangle2 po2rdcenter3 po2rgangle3 po2rmodelocal po2rnangle4 po2rncenter4 po2rres_max11.34 po2rres_min124.2 po2rtempfac0 delta map map itr mode estres angle undamp damp time cpu nptles ntot nmg defocus # cycle 2. Global POR, 1 iteration 6 r(g) # cycle 3. Local POR, 5 iterations 7 r(l) r(l) r(l) r(l) r(l) ° ° ° ° ° -6° -4° -2° 0° +2° +4° +6° +8° (Φ) ° ° ° °.... (θ)

2d. Auto3Dem ii. Local orientation refine (POR mode local) Penn State Med School – Friday, 4-Oct-2013 delta map map itr mode estres angle undamp damp time cpu nptles ntot nmg defocus # cycle 2. Global POR, 1 iteration 6 r(g) # cycle 3. Local POR, 5 iterations 7 r(l) r(l) r(l) r(l) r(l) iter 6iter 11

2d. Auto3Dem ii. Global orientation refine (POR mode global) Penn State Med School – Friday, 4-Oct-2013 delta map map itr mode estres angle undamp damp time cpu nptles ntot nmg defocus # cycle 2. Global POR, 1 iteration 6 r(g) # cycle 3. Local POR, 5 iterations 7 r(l) r(l) r(l) r(l) r(l) iter Å iter Å

2d. Auto3Dem ii. Global orientation refine (POR mode global) Penn State Med School – Friday, 4-Oct-2013 delta map map itr mode estres angle undamp damp time cpu nptles ntot nmg defocus r(l) # 4. Local POR, 5 iterations 16 r(l) # 5. Local POR, 5 iterations 21 r(l) # 6. Local POR, 5 iterations 26 r(l) # 7. Try some deconvolution in P3DR 31 r(l) # 8. Try some deconvolution in P3DR 36 r(l) # 9. Last round 41 r(l) iter 611.3Å iter Å iter Å

2d. Auto3Dem ii. Global orientation refine (POR mode global) Penn State Med School – Friday, 4-Oct-2013 iter 11iter 41 Needs more particles: - reduce noise - increase resolution

2d. Auto3Dem ii. Global orientation refine (POR mode global) Penn State Med School – Friday, 4-Oct Å Previously bin-by-2 data (2.76Å/pixel) Un-binned data (1.38Å/pixel) FSC(0.5) = 7.0Å; 0.26°; / 23248p 50 µgraphs

2d. Auto3Dem ii. Global orientation refine (POR mode global) Penn State Med School – Friday, 4-Oct-2013 iter 611.3Å iter Å iter Å no-bin 7.0Å Previously bin-by-2 data (2.76Å/pixel) Un-binned data (1.38Å/pixel) FSC(0.5) = 7.0Å; 0.26°; / 23248p 50 µgraphs

2d. Resolution Penn State Med School – Friday, 4-Oct-2013 FSC = Fourier Shell Correlation Correlation between two ½-dataset density maps in Fourier (reciprocal) space Measure of consistency vs spatial frequency (spacing) Consistency breaks down when common signal stops Problems ½-dataset density maps inferior to full dataset map Correlation limit – which? 0.5, 0.3, 0.143,… Consistency may be extended by systematic error including model bias “Gold Standard” (Henderson et al, Structure 20, 2012) ½-datasets analysed independently (RMC onwards) Correlation limit of DPR SSNR

2d. Resolution Penn State Med School – Friday, 4-Oct-2013 Over-fitting Solid – ½ datasetsDashed – full dataset vs xtal map Black – “gold standard”Grey – “classic”

2e. Interpretation & Modelling Penn State Med School – Friday, 4-Oct-2013

Summary Penn State Med School – Friday, 4-Oct-2013 Recipe for RMC/Auto3Dem RMC, check sections Auto3Dem – search, 10 iterations: ppftdelta_theta2 (or 3) Auto3Dem – refine (g), 1 iteration: po2rgangle2 (or 3) Auto3Dem – refine (l), 5 iterations: po2rdangle1.2 po2rdcenter2 – 5 Match dangle and dcenter to current resolution eg: 300Å radius particle, 1° ≈ 1°*pi/180*300  5 Å at edge So at 20Å, refine at1° angle steps 5 Å center steps (change to pixels) Repeat refine (local) and adjust dangle and dcenter down to match new resolution Use a realistic fraction of particles Use phase flipping (ctfmode 2) especially in po2r Use deconvolution (ctfmode 1) with modest tempfac in p3dr Run p3dr “by hand” to optimize data limits & tempfac