Three Dimensional Reconstruction

of Ryanodine Receptor Using

Random Conical Tilt Pairs


These instructions allow to perform a two-dimensional analysis and to create a 3D structure from images obtained on the electron microscope. Tilt pairs are selected with the command "particles" in web and windowed. The zero-degrees images are subjected to 2D alignment towards a reference. Multivariate classification analysis is applied to discern different groups. At this point, either a 2D analysis -including determination of the 2D resolution and four-fold symmetrizing a 2D average- or a 3D reconstruction can be performed. In the latter case, the 3D is obtained using the rotational parameters and the tilt single particles for a selected group. Procedures to determine the resolution of a volume and to four-fold symmetrize a volume are included.


updated 6/9/97, Montserrat Samso written 5/20/97, Montserrat Samso


Two-Dimensional Alignement with Reference and Preparation of Tilted Images
  • 1.Micrographs in the scanner format can be converted to Spider format using (b01.cpf) This creates files in spider format, one with original size and another reduced (use an even number, e.g.4).
  • 2.Choose command PARTICLES in Web and select pairs from the reduced size micrographs. Do the option Fit angles regularly, and check fitted positions. Choose 10-15 windows in representative areas where there are no particles, using option Background.
  • 3.Window images out from tilted (b01.twi) and untilted (b01.uwi) micrographs. This will create a continuous image series and correct for the different background in particles from different micrographs. A rotation equivalent to the phi angle is performed. The header of each image contains the tilt angle.
  • 4.Create a selection file for the whole image series. b02.sel
  • 5.Centration of particles by cross-correlation with reference. The reference can be a blob or a circularly symmetrized and low pass filtered version of an existing 2D average of the particle. Edit rcucent.ori to either create a blob or call an external reference. b01.ori Run it with oldspider.
  • 6.First round of orientational alignment towards a given reference. a. Mask and low pass a good average of hte sample interactively using @rcal1pre to create the reference. Run it with oldspider. b. b02.ori. This batch file calls the procedure @rcal1h Run it with oldspider.
  • 7.Get aligned untilted and tilted images. Second and third round of orientational alignment using average of first alignment as a reference; sum of all rotation and shift parameters, centration of the tilted images respect their 0 deg pairs, get individual rotational angle on the headers. a. Create average of resulting oriented images using spider command AS b. Mask and low pass obtained average interactively using @rcal1pre. Run it with oldspider. c. b03.ori. Run it with oldspider. This batch file calls the following procedures: rcal1h sumcddd rcsumshh rclabel2mod rctcenh
  • 5-7.If there is an existing reference for this set of particles (e.g. we are adding more particles to the dataset), steps 5-7 can be done at once as we already have the averages, using the batch file b04.ori The only precaution is that there has to be the first image of every series, as the procedure use it to know the file dimensions. Run it with oldspider.
  • 8.Check that the final aligned images are correct (e.g., displaying the averages and variances): b05.ori and delete all intermediate files.
  • 9.Remove unaligned particles and junk, and copy the good (aligned) ones to another directory in a continuous file series. a.In WEB use the Categorize command by displaying the image files and manually clicking on each bad particle under category 2 and saving the file numbers into a document file. It is better to repeat the same process for the tilted images and saving the file numbers in the same document. b.In spider, run operation AT IT to put the particle numbers in a consecutive order. Check the total number of bad particles. c. Run b06.sel Go to the next step (classification).

    Multivariate Classification Analysis of the 0 Degree Images

  • 10.Low pass filter the 0 deg projections. b85.fqu
  • 11.Create a mask. BC of a final average low pass box 10,10, filtration 0.5 FS and get average value TH M above average value BC of the resulting mask FS and get average value TH M below average value
  • 12.First part of CORAN. This file has to be run in the directory where we want the IMG file. b01.cas
  • 13.Second part of CORAN. This file has to be run in the directory where the IMG file is. b03.cas
  • 14.Check the first 8 factors (specially clockwise/anticlockwise) with the procedure evalcoran, b01.eva
  • 15.Get different groups of particles. a.In web, use command Dendrogram and check a good threshold to differentiate between clockwise and anticlockwise projections. b.Run CL HE to create selection files. c.If it's necessary to join 2 groups but excluding some other and it's not possible doing it by CL HE, join the selection files by editing them. Use a batch file to add a constant to the key number. b03.sel d.Run AS DC to get averages for every group. e.Run AS R to get averages that can be used in the t-test, useful when two-dimensional averages are compared.
  • 16.Create 2 parallel directories for clockwise/anticlockwise particles. b05.sel

    At this point either Two-dimensional analysis or Three Dimensional Reconstruction can be performed.

    Two-dimensional Analysis

  • 17.Get the resolution for every average. b03.rf2.
  • 18.Four fold symmetrize averages using the procedure rav2dal, b01.4fo.
  • 19.Comparison of two samples with the same overall conformation which differ in a certain area, using DR DIFF. a.Obtain reference using @rcal1pre. b.Orient the four-fold symmetrized images. b14.ori. c.Create a mask as described in point 11. d.Normalize images. b01.cor. e.Stack 3 images in order to make a volume, and perform DR DIFF. This operation works only with volumes. b01.stk. f.Pick a slice of the volume containing the difference PS Z. g.Four fold symmetrize. b01.4fo. h.Threshold this slice to select positive differences. TH M. Use the option "below". A good value to start is the average value of the image.

    3D Reconstruction Using Weighted Back Projection Algorithms
  • 20.Remove unaligned particles and junk, and copy the good (aligned) ones to another directory in a continuous file series. a.In WEB use the Categorize command by displaying the image files and manually clicking on each bad particle under category 2 and saving the file numbers into a document file. It is better to repeat the same process for the tilted images and saving the file numbers in the same document. b.In spider, run operation AT IT to put the particle numbers in a consecutive order. Check the total number of bad particles. c. Run b06.sel
  • 21.Create 2 parallel directories for clockwise/anticlockwise particles. Create a selection file for each (clockwise/anticlockwise) series. b02.sel Copy particles to each directory using a continuous series and perform 3 rounds of BP XY and altovol using batch file b01.rec. Run it with oldspider.
  • 22.Calculate total shift and compute 3D using back projection algorithms. b02.rec Run it with oldspider.
  • 23.Take a quick look at the reconstruction (low pass filter, invert sign). b03.rec.
  • 24.Four fold symmetrize volume. This batchfile calls the procedures rav3dal cmask3d3 b04.rec Run it with oldspider.
  • 25.Split select file used in 3D into two separate select files to be used in the following two 3D reconstructions for comparative purposes. b05.rec
  • 26.Compute two 3D reconstructions of the odd and even particles for clockwise set. b06.rec
  • 27.Compute two 3D reconstructions of the odd and even particles for anticlockwise set. b07.rec
  • 28.Compare the two half volumes b08.rec
  • 29.Use gnuplot to view the resulting curve Plot 'rfdoc' using 3:5 with lines