Localized Refinement in cryoSPARC (NSP15 Case Study)

[@willj7131]

just to confirm, I think the coords given by chimera measure center is in angstroms instead, that makes ur calculation right to give pixels for cryosparc input. Thanks for the tutorial!

[@MuhammadKhan-dv7dw]

you did not explian the symmetry expansion and the final step where how to merge all the data after the individual refinment

[@_Sree_Subham_Astrology]

Thanks It helps a lot. What is the next step? How to combine the maps for model building?

[@tpf4292]

when finding the center of mask, was it pixel-to-angstrom conversion? why did you divide, but not multiply, by pixel size?

[@abhisekmndl]

Does it work on small proteins like 60kD or it’s good for big proteins only ??

[@cryoemtutorials5598]

Localized refinement just ignores signal from outside of your mask when it is aligning your particle images. As the size of your particle decreases, there is less and less signal contributed from each image to the final map. So it is still worth trying on any protein, but the benefit of localized refinement for something small will likely not be as noticeable as the resolution increase for a large protein. If you are working with a small protein, I would say that the biggest increase in resolution outside of having more particles, would be a more homogeneous data set. Try optimizing for that with 3D classification.