I'm sorry, I'm having a very difficult time understanding what you are after. There seems to be some confusion about the specific definition of words, which is impeding the discussion. "modeled before by other method (e.g. cyrstallography)". To be clear, crystallography and cryoEM are experimental techniques, not modeling techniques. Modeling implies structures derived from theoretical not experimental methods. You say "for evaluating our work" but have not said what sort of work you are talking about? Have you collected CryoEM data, and you are hoping to simulate some date from PDB models to better understand what you are seeing? Are you software developers trying to test an algorithm? Something else entirely?
My best guess from your statement is that you are trying to create synthetic CryoEM data for some purpose. If that is the case, then there are a range of different extents to which you may go in the simulation process, but even the most thoroughly simulated data still fails to capture all aspects of the experimental system. That is to say, when testing software, even if you use the most realistic simulated data, results are always far better than with real data, as there are simply some aspects of real data which are difficult to capture.
Is there a reason you don't wish to look at real experimental data? EMPIAR (
https://www.ebi.ac.uk/pdbe/emdb/empiar/) contains an archive of raw image data associated with published maps, most at near-atomic resolution (so a PDB model also exists).
If you really want to do "simulation", then it depends on how thoroughly you wish to model the imaging process and specimen. The steps are:
1) convert the PDB to a density volume (e2pdb2mrc.py)
2) make projections of the volume (e2project3d.py generally with a random orientation generator, e2help.py orientgen)
3) apply a CTF to the projections and add noise (e2proc2d.py ... --process math.simulatectf:... (see e2help.py processor simulatectf -v 2))
There are even more steps if you wish to add these into a simulated micrograph and/or simulate conformational variability, buffer molecules, impurities, radiation damage, etc. A few people in the community have developed programs specifically designed to simulate data, but again, if you use such simulated data in software testing, simulated data is much too perfect to be realistic.
Is that closer to what you are trying to find out?