Processing tips from my publication of a highly dynamic enzyme

Hi all cryosparc fans,

I want to sincerely thank you all for your helpful discussions and tips during my recent postdoc.
The protein I worked on (angiotensin-I converting enzyme) was highly dynamic, relatively small, heterogeneous, and heavily N-glycosylated which posed a number of challenges during image processing. It was really tough to learn cryo-EM remotely from scratch during the pandemic so this forum was a lifeline! Thank you very much :slight_smile:

Here is the link to our Open Access publication in The EMBO Journal: Cryo‐EM reveals mechanisms of angiotensin I‐converting enzyme allostery and dimerization | The EMBO Journal (

Briefly, what made it possible to solve the structures of both monomeric and dimeric ACE from a single dataset (where the dimer was a tiny, tiny fraction which we could not separate biochemically) was:

  1. initial generation of a 7A structure in RELION by template-based autopicking, iterative 2D classification, and iterative 3D classification
  2. TOPAZ denoising of the micrographs
  3. training of a TOPAZ picking model using the 7A particles as positive labels and iteratively improving the model by manual curation and retraining (thanks @tbepler @alexjamesnoble )
  4. a single round of 2D classification of all TOPAZ-picked particles before going straight to 3D ab initio and 3D heterogeneous refine in cryoSPARC
  5. extensive 3D sorting in cryoSPARC to separate out different conformational states of the monomer, and separate the monomer from dimer
  6. 3D variability analysis to identify regions of hinging in the monomer and dimer. This guided mask creation for step 7
  7. local refinement with the non-uniform refine algorithm in cryoSPARC to focus on a particular domain (without subtraction for the monomer but with subtraction for the dimer since dimerization drastically increased the non-interacting domains’ dynamics) @mmclean @olibclarke
  8. focused 3D classification w/o alignment in RELION to minimize the heterogeneity in the locally-refined map @DanielAsarnow
  9. further local refinement in cryoSPARC
  10. it was ESSENTIAL to use very soft masks to avoid overfitting artefacts due to the high N-glycan content. The masks were dilated to include the protein but exclude most of the glycan density, and then padded very softly to extend beyond the glycan density. @emil

3DVA gave us valuable insight into how intradomain allostery could occur between the different parts of this two-domain enzyme. Although the resolution is not atomic, we hope that the insight we gained could guide future studies into allosteric regulation and dimerization-induced intracellular signalling of ACE.

I hope that my work can assist someone on this forum who is struggling with processing of a similar sample :hugs:

monomeric ACE
movie4 (1)

interacting domains of dimeric ACE


Wonderful story, congrats!

Best wishes,

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Really grateful for the powerful Topaz!

Very nice work, congratulations! :slight_smile:

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Thanks again @olibclarke!

This is great!

Perhaps a tutorial on mask generation with images would be useful for folks getting into EM as well. “softness” can be difficult to visualize without examples.


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ACE in the holes! Congratulations and beautiful work!

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Thanks @gdodge!
Mask creation was certainly a lot of trial and error to get ones that worked well. The exact settings and mask shape/boundary will depend on each sample. My earlier post with input from @mmclean has more detail and also includes the link to the updated cryoSPARC tutorial page.
See the last few messages in this post: Masking out glycans - #12 by lizellelubbe

Hope this helps!


Hehe :wink: thank you @user123!

Great work. Congratulations. The information you listed for your data processing is extremely valuable.

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Thanks @yurotakagi! Hope your image processing will be successful :slight_smile:

Hi everyone,
The EMPIAR entry containing the raw movies of our ACE dataset has now been released and is accessible here:


Congrats @lizellelubbe, and thank you for posting about your processing journey here!


Thank you @apunjani !