Thursday, March 19, 2020

Optimization of Data Independent Aquisition Experiments with Predicted Spectra Libraries!

(Nope. I don't know what that is, but I'm borrowing it because it's super cool looking and I don't see anywhere that says I can't use it.)

Last year at ASMS it was PROSIT, Prosit, PROSIT!!, Prosit, Prosit. Deep learning for spectral libraries. And you know what I feel bad about? The fact that there are 3 or 4 other things out there for deep learning spectral libraries that are also AMAZING and I can't name them off the top of my head. I'm going to work on that. PROSIT is the most recent and it draws off of ProteomeTools which is synthesizing EVERY  HUMAN PEPTIDE. So...that's a pretty big advantage. Plus it is so easy to use that I can do it.

Prove it? Here is a walkthrough that I made. Bonus Shia LaBeouf (caution, music)

How far can you push Prosit spectral libraries? Here is a brand new pressure test for Data Independent Acquisition (DIA): 

Why would you read it?

Single shot human with E.coli peptides spiked in
Q Exactive (the regular ol' D30 Q Exactive -- okay, it's a Plus, but the quad is a little better and it gates smarter.)
>8,000 proteins quantified

How do they get there?

SPEEEEEEEED digestion (me rambling about it here)
uPAC columns (great, albeit kind of long chromatography -- shortest gradient is 160 minutes)
Optimization of targets based on Data Point Per Peak (DPPP...which...isn't an acronym I can imagine using again)

What's that do?

It reduces the size of an in silico digested peptide library from over 3e6 precursors to around 2e5 precursors.

Don't believe me? Check out the data yourself. It is all up on ProteomeXchange as PXD017639

And -- they didn't use SpectroNaut, by the way! They used DIA-NN (Neural Networks) which you can get from Github here!

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