Heterogeneous reconstruction of small protein

[danielmarr]

Hello,

I’m working on a protein (90kD) containing multiple domains connected by several long, flexible regions, and I’m trying the HR-HAIR strategy described by professor Oliver B. Clarke .

Using the parameters from the paper, the ab initio reconstruction shows poor secondary structure. Interestingly, at around 3,000 iterations, the reconstruction looks much better, with recognisable β-sheets. However, by around 4,800 iterations, it becomes worse again. Now it keeps going.

I also found that my 2D classification is much better with “Enforce non-negativity” enabled, whereas adding “Use clamp-solvent to solve 2D classes” together with “Enforce non-negativity” makes the classes considerably worse.

Has anyone encountered similar behaviour with highly flexible, multi-domain proteins? In particular, is it reasonable to use the ab initio reconstruction from an earlier iteration when it looks better, rather than the final reconstruction? Is it possible that the heterogeneity makes reconstruction difficult?

Any advice would be greatly appreciated. Thank you!

version: 5.0

[carlos]

Hi Danielmarr,

@danielmarr

“multiple domains connected by several long, flexible regions”

is the way to hell… unless you have some backfolding, you might have more success going back to the bench and making more rigid constructs. There are several strategies using binders for that kind of situation.

That said, if you want to try stuff on this dataset: try finding back folded classes and working with them only. This is still not a case for beginners because the protein is in the small side for cryoEM.

(I’m still curious about what the others have to say)

[Mrs.Smith]

Are those really 2D classes of particles? I am a cryoEM beginner myself, but if I saw those 2D classes and they would become worse with iterations, I would think it is just noise. How does one know whether what they are looking at is particles vs. noise?