Is there a way to limit the number of particles per 2D class during 2D classification?
We have a protein that has a big and stable part and another big part that moves significantly relevant to the first one and in a subset of the particles falls off. We have cleaned the dataset from junk particles and are trying to disperse the particles among 2D classes but the program over-aligns everything on the first part until the floppy part is averaged out and appears as cleaved off fuzz in 3D that cannot be refined locally or brought back by 3D classification.
During 2D reclassifications we end up with more or less the same number of ‘good’ classes, where the rest remain as classes with zero or few particles even if we double or triple the total number of desired 2D classes (so no gain by increasing the number of classes). We tried playing with the initial uncertainty, masks, align resolution, etc. but cannot really get the particles to not converge. If we select a single class we can break it up in subclasses somewhat more efficiently and we see high resolution features in the floppy domain (so it is not like it is not there) but we would really like to have such dispersal in dataset scale.
Bottomline, is there a way to limit the maximum number of particles per 2D class so that we can force the program to keep things separate and try to align over the entirety of the particles? If not, any other suggestions will be greatly appreciated.