I’ve found a ton of really useful tips on here for processing small particles in 2D, ab-initio jobs (thank you!). I’m working on a case right now where I am trying to use heterogeneous refinement to separate my true particles from junk for a ~75 kDa protein using a semi-reasonable ab-initio class and three “junk” classes. The protein has fairly strong features from a top view, but not in the side views. I’m worried I am throwing out side views because of this. Any tips on how to set up the heterogeneous refinement to maximize the precision in cases like this?
Hi,
75 kDa protein and if its a C1 symmetry will make processing challenging. I would recommend first use the right box size for particle extraction. Then use the cryosparc 2d classification protocol for small particle. Do increase iteration 60-80 depending on number of particles. Make 4-5 ab initio classes with resolution 8-12A. Take your probable class for NU refinement.
Next with the refined volume as a input as one volume and rest 4 from Ab initio output, carry out a heterogeneous refinement with all particles used in 2d classification ( not select 2d) until there is no improvement in your main volume. This will take care of all the other views that we may miss while selecting 2d. Then finally do NU refinement, Nu-defocus and ctf and finally sharpen with either cryosparc or DeepEM enhancer. You can also tk half maps directly from final refinement to Phenix map sharpening.
Hope this helps.
Best
Hi Cameron,
When I do heterogeneous refinement, I sometimes find that the “good” class still looks noisy or has some overfitting artifacts. After each iteration of heterogeneous refinement, it can help to redo ab-initio to get a better quality map for the next round of classification. When the junk classes start to account for <5% of the particles, I find that it’s usually worth redoing 2D classification with 100-200 classes, 40 O-EM iterations, 5 final iterations, and a batch size of 200. Since most of the junk has been removed, I usually get more diverse classes of the protein, making it lot easier to identify rare views. Hope this helps!
Best,
cbeck
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