Cannot align small protein complex particles

Hi All,

thank you for all the good advice! Several rounds of 2D classification to “purify” the particle set and extensive experimentation with the 2D classification parameters has led to some pretty recognizable views of a beta propeller. I found that marginalizing over poses and shifts was key, increasing the number of iterations and batchsize (40/400) as was finding the right threshold for pixel values to use in recentering the particles (as high as 75%). This last parameter is important in dealing with contrast from neighboring particles causing the alignments to drift.

As you can see, there are “shadows” from neighbors appearing at consistent locations in random orientations. Nevertheless, I could get an ab initio model that looks relatively reasonable (or does it?):

It appears that allowing the particles to sort into 3 ab initio classes and increasing the minibatch sizes is helpful. However, I cannot get a 3D heterogenous or 3D homogenous refinement to work. I’ve tried defining masks of various sizes to limit the influence of neighboring particles on the alignment, and reduced the lowpass filtering of the initial ab inito models, but with little effect on the outcome.

I know I’m not working with easiest data (dense, small 50 kDa particles), but given that the particles can be aligned into a variety of views in 2D raises the question of why the same should not be possible in 3D.

I’d be grateful for any observations and suggestions to put me on the right track!

Cheers,
Stefan

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Hi Stefan - what parameters are you using for ab initio? The defaults (initial resolution 35, final 12) are often not great for small particles with few features at low resolution. Often starting at 9Å and going to 7Å, or even starting at 7 and going to 5, gives better results. Increasing the batch size can also help. E.g. for C1 ab initio of apoferritin, I find that increasing the batch size to 1000 helps with obtaining an isotropic reconstruction from ab initio.

Also for 2D, you might try a tighter mask to exclude some of the neighboring particles.

Cheers
Oli

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Hi Oli,

thank you for your (as always) fast and informative response. As far as ab inito goes, I have been testing different resolution parameters. The results I have shown above come from a 35Å/7Å intial/final combination. I will keep reducing the initial resolution; thanks for the tip!

My initial/final minibatch size has been 400/1200. Should I increase the initial batch size further? Is there a benefit to starting with fewer particles per batch other than speed?

With regards to masking 2D classes, I have tried it and a tighter mask does make the classes look cleaner, but there is no apparent benefit to the quality of the averages or appearance of new views. The output number of particles in good looking classes is comparable between 2D classifications with and without masking. My understanding is that the particles do not get altered by 2D masking – would that help with downstream steps?

Cheers,
Stefan

Hi all,

I am dealing with a similar problem and this thread has been very useful so far (thanks @olibclarke!).
I am stuck at the same step as @stefan. My 2D classes look very similar to the latest ones
shown here, but I can’t get any reasonable ab-initio or Homo/Hetero refinement to work properly.
Are there any updates on this dataset?

Best,
twg

Hi @twg,

Unfortunately, the short answer is: no. I’ve tried 3D classifying with Relion to identify heterogeneities, but without success. It seems to me that there is not sufficient singal to align the particle views robustly. I’d love to hear more ideas for troubleshooting, or if you manage to make progress.

Cheers,
Stefan

Hi :slight_smile:,

I’m facing more or less the same problem. I’ve collected a dataset of a small and elongated protein complex (~75 kDa) with good contrast too. We align the 2D but we have issues with the 3D.

I attach a pair of micrograps for reference:

They were collected on a Talos Arctica with a K2 at 36 K magnification (1.2 A practical, 1.13 A corrected). One shot per hole on a 1.2/1.3 Au grids. Defocus was settled between 0.5 and 1.7 um. The dose is 50 over 65 frames (0.77 e/A/frame). I curate the micrograph to select the ones with ctf fits better than 5 A and I extracted around 2 million particles.

I use previous data to pick particles and I extracted them with a 320 box size.

I run a 2D classification and I get good 2D classes (image after several 2D classification to clean the dataset):

*Even though, I was trying to use the parameters described here to maybe improve my 2D classes (I’ll explain that after).

But then, when I move to the Ab-initio and try the 3D reconstruction (1, 2 and 3 classes) I don’t get good models.
1class

Either with the NU-ref.


Screenshot 2020-07-31 at 12.16.54

Even if the particles are small I think the 2D classes are kind of promising and I think it could be possible to improve the 3D model to get a good one. So, I tried the advises that are proposed here but for both 2D and 3D, i get the same error:

I understand it’s a lack of GPU problem, so maybe my job settings are too exigent or they make non-sense at some point, or maybe both.

My 2D parameters are:

  • Number of 2D classes: 80
  • Max resolution: 3
  • Initial classification uncertainty factor: 4
  • Re-center mask threshold: 0.1
  • Re-center mask binary: ON
  • Force Max overr poses/shifts: OFF
  • Number of online-EM interations: 40
  • Batchsize per class: 300
  • Number of iteration to anneal sigma: 25
  • Cache particle images on SSD: OFF

And using my previous 2D classes I was trying to run an Ab-initio job with the following parameters:

  • Maximum resolution (Angstroms): 9 (before I tried 7)
  • Initial resolution (Angstroms): 12 (before I tried 9)
  • Initial minibatch size: 400
  • Final minibatch size: 1200
  • Cache particle images on SSD: OFF
  • (For 1, 2 and 3 classes).

If someone have any suggestions or ideas I’d be really happy to try them :slight_smile:.

Lu.

Hi Lu,

Classes look nice! :slight_smile:

What GPU are you using? I wouldn’t expect you to run out of memory using those settings on a 1080Ti or 2080Ti…

Also how many particles do you have remaining in the NU-refine you show here? Maybe need a bit more cleanup in 3D?

Oli

Hi Oli,

Sorry for the delay, I was trying to figure out which GPUs we have, apparently the GPUS used on the server are K80 Tesla cards.

In that NU ref I have only 173329 particles, since I select only a few classes to avoid my preferential orientation problem.

Lu.

They should definitely not be running out of memory then! that card has 24GB, should be more than enough for such small particles

Might be a long shot, but depending on your server setup, there could’ve been another user or process using the GPU memory when you tried to use cryosparc. Try “nvidia-smi” in console to see the memory usage. If your cryosparc is implemented in a node-based configuration (rather than just a single server) then this suggestion won’t apply to your case.

In regards to box size, the old rule of thumb was to use a box that’s 3x the diameter of your particle. The box size is about the same as what you get using the mentioned equation -just easier to remember.

Very helpful thread!

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Hello @Ulvetanna did you manage to solve the issue with ab-initio?

Dear All,
To follow up on this topic. I also ran into the similar issue with one of sample with small size. Protein size 65A, pixel size 0.671, boxsize 384->192, defocus 0.4 to 2 um.
I have tried

  1. Force Max over poses/shifts ON, EM-iteration 20 and Batchsize per class 200. I got streaky classes.
  2. Force Max over poses/shifts OFF, EM-iteration 20 and Batchsize per class 200. I got smooth spheres with no feature.
    Anything wrong? Your suggestion are much appreciated.

Your first run with force max on is somewhat promising. Try to increase iteration to 40 and use a circular mask ~1.2x the diameter of your particle

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Thanks for quick reply. I am still trying.
What confused me is that many mentioned to turn force max off, which significantly improve the quality of 2D. How come I got pure spheres, not sure where is wrong.
Best Regards,

Hi Jamon - if you turn force/max off, you will need a lot more iterations for it to converge. This picture is from iteration 8 - how did it look after 20 iterations? I would usually run for 40 iterations with force/max off.

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Good morning
following on the thread here: I have a small complex (~65 kDa), 2D classes look good, and the ab initio produces a reasonable model. But I fail to go past 5-6 Ang during refinement, and the maps looks either featureless or (if I look at the sharpened map) a total mess (over-refinement is a word that comes to mind). I tried masking and local refinement and again the maps don’t make any sense. Any suggestion will be appreciated

Giovanna

Hi all,
I have same issue with 2D classification and Ab-initio to build model in my project as shown here.
My protein is not too small, around 200 kDa, the size should be around 100 A including the membrane micell. I tried to build 3D with Ab-initio but I could not get any reasonable secondary structures like helix or transmembrane domain. I includedd some 2D classification and select 2D here, could you please give me some advice to further select 2D and ab-initio?


Thanks a lot,
Nguyen

Hi Nguyen,

This is too small and featureless to have 200kDa of ordered mass - these look like empty micelles or maybe a very small transmembrane protein. Do you expect a significant extramembrane domain?

Cheers
Oli

Hi,
Thank Oli for your comments!
The cicular mask diameter is set 150A. My expected particles are a protein complexes. Yes, the lower classes look much smaller than 100A. But, do you think the upper 2D classes might belong to transmembrane proteins or complex?