Image processing and 3D reconstruction

Use cryoSPARC for the following steps except those particularly mentioned.

Do motion correction by [Patch Motion Correction]

Bin 2x in fourier cropping for super-resolution video stacks

Bin 1x in fourier cropping for regular video stacks

Do contrast transfer function determination by [Patch CTF Estimation]

Remove the outlier micrographs base on the estimated defocus and resolution value.

Do particle picking by [Topaz]

Manually pick 10 micrographs as learning dataset

Optimize the 'picking threshold' with the 10 mics

Apply the parameter to the entile dataset

Particle extraction

Use the box size 1.5 times larger than the target particles

Bin 4x to facilitate the following classification jobs

2D classification

Set 50-100 classes dependent on the data size

Remove the obvious junk particles

Obtain an initial model

[1] Use Ab-initial (Optional) only select the 2D classes that show high-resolution features

[2] Use previously determined structure if it's available

[3] Create a new medel by AlphaFold

Do 3D classification by [Reterogeneous Refinement]

Low-pass your model to 15-20 Å

Run the job with 2-3 junk models

Run multiple times (typically 2-4 rounds) until the result converges

Re-extract the particles with

bin 2x for super-resolution video stacks

bin 1x for regular video stacks

Do 3D reconstruction by [Homogeneous Refinement]

Repeat 2-3 times until the resolution converges

Check whether the FSC curve is healthy

(Optional)

Do CTF refiment followed by homogeneous refinement.

Check whether the resolution get improved

(Optional)

Do local refinement if the map contains multiple rigid sub-regions

Decide the masks based on [3D Variability] or [3D Flex]

Use [ChimeraX] to create the maps

Use [EMAN2] to compose the final maps at the end