Small membrane proteins: 2D classification and ab-initio reconstruction

Cryo-EM Data Processing
2

Hi @M.Grieben,

Thanks for your post - this is an interesting case.

  1. The parameters you chose for 2D classificaiton are appropriate for the context.
  2. The batchsize-per-class can help here, and in fact you can turn it up to a very large value (say 1000). This will make the job slower, but it will ensure that the signal from more particles is available at each iteration of classification. You can also play with the “Initial classification uncertainty factor” - a larger value will cause classification to remain uncertain about particle assignments for longer, allowing more exploration to separate otherwise similar particles into more classes.
  3. How sure are you that the protein is actually symmetric? Do you see any other recognizable features in the ab-initio reconstruction? It is important to note that the “resolution” values in ab-initio reconstruction are not resolution estimates and there is no way to objectively measure resolution at the ab-initio stage. Have you tried refining the structures/subsets of particles that you got from ab-initio? This will use a gold-standard split and give some more hints about what is happening in the data.
    Otherwise, the parameters you have chosen for ab-initio reconstruction are correct for this context. I’m assuming you’ve already tried with just 1 class and haven’t got something that seemed correct.

For your main question, it is possible (though a little cumbersome right now) to resample particles evenly across views. You can:

  1. Complete 2D classification in a way that resolves the most number of different “views”
  2. Run multiple “Select 2D” jobs from the class 2D output, and in each one, select a different subset of classes, that share a similar view. e.g. select top views, then side views, then front views, etc.
  3. From the output of each Select 2D job, run a “Particle set tools” job. Change the “split batch size” to a number of particles that you wish to take from each separated set of particles (say 10,000) , and turn on “split randomize”. This will randomly select 10k particles from each viewing direction.
  4. Create an abinitio job (or any other that accepts particles) and connect the outputs from all the particle set tools jobs to that job, all connected to the particles input. This job will then operate on a “rebalanced” set of particles.

Let us know what you find!

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