Dealing with classes with defects (flexibility)

Hi All,

I am refining a dataset of a multi-subunit complex ( A + B + …). After heterogeneous refinement, I got two good classes. Although in one of the two classes, A is good, but B has some parts relatively weaker, while in the other class, B is fine, but A has weak density. For this case, is there some way to combine the good parts of both A and B together to generate one map with good density for both A and B? Alternatively, if I model and refine A and B against two maps from the two classes, respectively, how should I present it in a publication? I also tried combining both classes, and this resulted in averaging everything, with relatively weaker A and B than other parts. One way may be to do masked refinement (or local refinement in cryosparc) with the combined classes, but I am wondering if there is other strategies I could try? Any comment is appreciated.



Hi @huqi, hopefully this is still relevant in your project:
If you find two classes like this, it generally means there is a population of particles where A is more rigid/well ordered and a population where B is more rigid/well ordered.
It is definitely a good idea to refine the separately and would be considered common practice to include both maps in a publication and deposition to EMDB.
If you want to combine the particles (which might be useful in some cases to boost SNR) you can, as you suggested, use a local masked refinement. The hope is that if you mask only A or only B, and use all the particles, the particles where either subunit is “floppy” will align better when only considering A or B respectively. There is some chance that this would yield improved reconstruction for either subunit. However, there is also the possibility that in either set of particles, A or B is actually disordered, and this would not help in yielding better reconstruction.