The particle sets we are working with have an intrinsically low signal-to-noise ratio. While we were able to achieve reasonable 2D classifications, the results of ab initio reconstructions were inconsistent across different settings. Are there any effective strategies for generating ab initio reconstructions using particle sets with low SNR? Additionally, how can we validate that an ab initio reconstruction accurately represents the true particle architecture?

Based on my experience, the correct ab initio model often had a larger number of particles compared to other suggested models (out of 3 or 5), and subsequent homogeneous refinement quickly improved the resolution, yielding a reasonable density map.
Any suggestions would be appreciated.

I don’t think you should worry so much about the quality of ab-initio, as far as the refined map looks good. Ab-initio just needs to give you a starting anchoring point for the refinement, and it seems to be doing that for you. Or please let me know if I’m missing something.

I’m assuming that this is a relatively small particle?

If so you could increase the resolution that you use for the reconstruction, I believe that the default value is initially 35 A, down to 12A. I have used a starting value of 15 A and gone down to 5 A before for small particles. The way I interpret it is that at low resolution these particles may just look like a sphere, whereas at 15 A ab initio can observe some of the details needed to properly assign poses. 35 works for something like a ribosome, that has really distinguishable features., but maybe not in this case.

You could also increase the number of initial iterations/batch size so reconstruction spends more time with more information before deciding on an initial map.