[3dem] 2D classification resolution problem

[Takanori Nakane]

Hi,

Tilted data collection means that defoci vary a lot within a micrograph.
RELION uses CTFFIND, which finds only one defocus value per micrograph.
If the dataset is good enough, you can repeat CtfRefine and Refine3D
to bootstrap (see Fig 3 of https://urldefense.com/v3/__https://elifesciences.org/articles/42166__;!!Mih3wA!AnimFSo7qGvWrVPMUf3pJDzBeoDKRfRjvihyb6Diktr0NQutZuEqaIb6gXZVx8uSCtu5m9NBsxsf1BiZCFr5NAC1H3AmRizEXqA$ ). 
Otherwise, you might need local CTF estimation by other programs such
as Warp.

Even if you manage to get local CTF, a tilted grid means a longer
path electrons have to pass through; so this has the same effect
as thicker ice.

In my opinion, tilted data collection is the last resort against
preferred orientation problems. If you can resolve the issue by
other means (e.g. optimization of blotting parameters, additives,
slightly different construct), that would be much better.

Best regards,

Takanori Nakane

On 9/7/23 18:48, Dario Saczko-Brack wrote:
> Dear all,
> 
> I have acquired a small cryo EM data set of a protein (ca 120kDa) and 
> processed it with Relion and Cryosparc. Since the 2D classes looked 
> promising, but the orientation bias was very strong and the number of 
> usable holes was very small, I made new grids and acquired another, much 
> larger data set of the same protein at 0° and 30°.
> 
> For some reason, I dont get to a similar resolution as the first data 
> set. Any idea why? Would you say that is rather due to not optimal 
> processing parameters or is it due to bad ice quality? Meaning, is it 
> worth to invest more time into processing or into making new grids?
> 
> Best
> 
> 
> Dario
> 
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