Hi,
Is it possible to extract sub-particles from an Icosahedral reconstruction of a virus? I am referring to virtually any area on the viral surface and not just around symmetry axes or just the asymmetric unit. Do have to provide a mask for the region of interest in the volume alignment step, with the new center being the center of the mask?
Thank you.
AB
Yes, you can do this. Run symmetry expansion, then volume alignment tools with your desired mask and new center. Then when you extract using the coordinates out put by volume alignment tools, you will extract each subparticle in the virion.
Cheers
Oli
Thanks Oli. I will try it and certainly share my experience.
Best,
AB
Itâs easy to do, but I usually do this sort of thing in RELION because for the datasets Iâve experimented with, CryoSPARC converges too quickly and tends to leave some resolution âon the tableâ. RELION can (and usually will) continue to refine down to 0.02 degrees angular sampling, resulting in reconstructions which can be as much as 3 Ă higher resolution for really, really big objects; even with Ewald sphere correction enabled.
However, whether it will give you an appreciable improvement in resolution or map quality will depend greatly on how large your virus is.
Well, I typically use Scipion for this purpose. But I was wondering whether this could be done in cryoSPARC. So far I have always used Relion for the 3D classification followed by refinement. I have never gotten to resolution ranges beyond 3.5A with the kind of samples I work with. Even the best parts of the glycoprotein shell is around 3.5 A. I will try cryoSPARC and check the output.
Cheers,
AB
Have you tried local refinement in cryoSPARC after global refinement (e.g. after NU refine)? It generally gives a bit of a bump, and allows specification of the maximum angular sampling. This can be useful in cases where non-uniform refinement is beneficial (although SIDESPLITTER, a plugin for relion, can give similar results in some cases).
Yup, tried local refinement. It does help a little. The larger the object the less it helps, although there is a definite balance between box size for the blocks/focussed segments. Too small and it gets all confused and angles drift off (same happens in RELION), too large and there is little to no appreciable increase in processing speed (although obviously box sizes drop so crazy mega-systems arenât so essential).
I need to test SIDESPLITTER moreâŚ
Tested the sub-particle extraction and recon. Works nice. Have to try with some high res data to see how good the classification is. I have tried sub-particle extraction after NU refine (icosahedral recon) followed by 3D classification and refinement. In this particular case, the resolution was slightly better. Although, technically, it was not local refinement. Havenât tried SIDESPLITTER, need to try it.
Thanks,
AB
@rbs_sci Does CS local refinement still do poorly if you set these parameters?
Will test and get back to you on a run with those parameters.
Is there a tutorial/protocol/video for this? Similar scenario, I have an icosahedral virus with regions of localized asymmetry. Thanks in advance.
Iâm not sure re a tutorial - but have you tried the general procedure in this post? Did you run into trouble with one of the steps? Can explain further if needed
Hi olibclarke,
I am attempting to extract and classify subparticles from an icosahedral virus. I completed the following steps: symmetry expansion and vol alignment. I am attempting 3D classification and I am not sure about inputs. For particle stacks - I am using the output from the âvol alignment toolsâ, initial volumes - I am using the output vol from ab-initio, solvent mask - I am leaving blank but I am open to any suggestions, for Focus mask I am using the padded mask that I previously generated. Does this sound correct? Should I also impose Icosahedral symmetry or leave it at C1?
Thanks in advance.
Hi,
After volume alignment tools, you will likely want to use the resulting shifts to extract subparticles from the original micrographs.
Alternatively, if your original box is big enough, you can use the downsample tool to crop out subparticles from the particle images. You can then reconstruct in the smaller box, and proceed with 3D classification.
You shouldnât need to use volumes from ab initio at this point - you already have good alignments. Similarly, the whole point of extracting subparticles is to perform localized asymmetric reconstruction and classification, so you wonât want to enforce symmetry (unless there is local Cn symmetry within the subparticle).
Hi,
thank you for your response. Follow-up questions, if I am extracting sub-particles from the original micrographs do I use a smaller box (double the size of my focus mask?) or should I use the original box size I used to extract the whole capsid? Second - what is the relationship between the extraction box size and Fourier crop to box size?
In your alternative method you state âreconstruct in the smaller box before 3D classificationâ, am I missing a step/multiple steps between subparticle extraction from the micrograph and 3D classification?
Hi,
Yes, you probably want to use a smaller box for the subparticles - apart from anything else, because of the symmetry expansion it will be computationally very expensive to use the original box size.
The extraction box size is without fourier cropping. If you then wanted to make the images smaller, to make computation faster, you can fourier crop them.
Re reconstruction - I was suggesting to run a homogeneous reconstruction of the subparticles after re-extraction, mostly as a sanity check to make sure everything is working prior to 3D classification. Not strictly necessary, as Class3D itself will also calculate a consensus reconstruction at the beginning of the run.
Cheers
Oli
Hi,
great, thank you. I really appreciate all your help. I will keep you posted.
Best,
Dendena
Hi Oli,
the 3D classification is done and I am really excited about the classes generated, I can actually see additional density at a region that was just too flexible to see anything. In order to improve the resolution of the volumes what would be the next step, since I did symmetry expansion it is suggested not to do NU refinement or homogenous refinement. So I guess heterogenous refinement?
Great! You want to go with local refinement after symmetry expansion - NU, homogeneous or heterogeneous refinement all do global orientational searches, which can lead to superimposing symmetry-expanded copies on one another - which leads to spurious correlations in the FSC.
Cheers
Oli
Hi Oli,
thanks will keep you posted.
Best
Dendena