Hi @rbs_sci,
Could I ask you for some more details on the classification parameters you used? In particular:
- since your map was <2 A, did you use a filter resolution around 2-3 A?
- did it help to use more than 2 classes?
- did you play around with the class similarity parameter?
How much mass did your mask include? I assume that classifying the presence of a ligand would require high filter resolutions (~3 A), but I’d imagine that the larger the mask, the more likely it is for the classification to pick up on 3 A movements elsewhere in the mask. Would you increase the number of classes to compensate? How many particles did you have to work with?
I’m also seeing the same thing as Oli where smaller masks around the ligand for 3D classification inevitably lead to artifacts, presumably because there isn’t enough signal inside the mask to compare against. I’ve also found that I get the best results if the solvent mask is the same as the focus mask around the ligand - if I use a mask around the entire complex as the solvent mask, I end up getting equally-sized classes, regardless of whether I turn on force hard classification (this thread seems relevant).
In my case, I’m dealing with a challenging dataset where a small-molecule ligand intercalates between two DNA bases and shifts the register of the downstream DNA by almost exactly 1 base pair. At low resolutions, it’s hard to tell whether the ligand is bound at all, since it occupies the same position as a nucleotide in the apo protein.
I currently ~2.7-2.8 A map, and it appears to be an average between the ligand bound and unbound states, (i.e. an average between the nucleotide and the ligand) but I’m having trouble classifying these states into two distinct classes. I’d appreciate any advice you or anyone might have on classifying small ligands.
Cheers,
cbeck