Hello @egelman,
Thank you for the post! Definitely cryoSPARC’s ab-initio and refinement algorithms are susceptible to this fundamental limitation when reconstructing helical lattices. Fully a-priori helical reconstruction without symmetry knowledge is experimental and empirically can work in some cases (as you mentioned) but can’t be guaranteed to work in general. Incorrect solutions can be converged to both in ab-initio reconstruction and refinement. In the EMPIAR-10031 case study, we presented our recommended workflow of asymmetric ab-initio / refinement, followed by symmetry determination, as an example of a workflow that we’ve seen to work on some datasets, but this does not work generally for all datasets for the exact reason you have pointed out. Because of this, structures should always be inspected visually before any further processing or analysis is done. And of course there are tools in other software packages that users have found useful for large-scale symmetry determination, e.g. the SPRING packages’ SegClassReconstruct program (based on determining symmetry from a single 2D class).
Your comment on run-to-run variability is also behaviour that we’ve seen too – ab-initio and refinement both use random seeds (which can be specified as parameters to each job) to govern density initialization and batch selection, so these seeds can be specified to control all the random choices that the algorithm makes. In practice however, accumulated floating point non-determinism on the GPU can still cause some run-to-run variability even if the same seed is specified.
In our helical processing documentation we have notices for the above issues, and we also include some suggestions for parameters of either job that tend to generally help (e.g. in ab-initio, forcing the use of high-resolution information early on sometimes produces better results). Most of these are highlighted in the guide page titled Helical Symmetry in cryoSPARC, but these are mainly suggestions that we’ve seen to help in our limited experimentation so far.
Best,
Michael