I have imaged and have maps of a membrane protein under 2 conditions (apo and +ligand). Each subunit of my protein has 2 domains (A and B). Under condition 1 the map is clearly symmetric but in condition 2, the domain A is symmetric between the 2 subunits and good resolution (3-3.5) but subunit B does not appear to be (low resolution and is only visible in one subunit of the dimer). May be in condition 2 there is a mixture of states (say one in which domain B is symmetric and another where it truly isn’t) that I have not been able to separate as yet. I was hoping to get advice on strategies to better classify/perform reconstruction on cryosparc. I have ~1.1x10^6 particles so I think I have room to play here. My ideas were:
(1) Ab initio reconstruction with multiple classes:
- test with different values of class similarity scores. (I have thus far only used default value 0.1. Has
anyone tried other values which gave better classification?)
- test maximum alignment resolution. (I have only ever used the default value of 12 but may be I can
use lower like 6? My current overall map resolution goes to around 3.9A currently)
(2) Heterogenous refinement with C2 enforced, then take each class and do homogenous/NU/local refinements with C1 to test whether C2 features are retained in the various classes.
(3) If I do symmetry expansion and with the expanded set of particles a local refinement, then only the symmetric domain is expected to get better, right? How could I resolve the unsymmetric domain B better? Subtract density of the symmetric domain and then classification (via ab initio recon and heterogenous ref) with particles of subtracted densities?
It would be great to get suggestions from more experienced users.