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!
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.
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?
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?
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.
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.
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 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:
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.
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
Force Max over poses/shifts ON, EM-iteration 20 and Batchsize per class 200. I got streaky classes.
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.
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.
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
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?
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?
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?
,
