Wednesday, September 11, 2019
SWARM-- Remove the adducts and clean up the data!
I'm not sure I get it. I probably shouldn't admit this, since the authors call it "straightforward" twice.
Wait. 3 times! And I think I get it now! Go espresso go!
Here's the problem: When you use ESI to ionize a protein you always get some dumb adducts, particularly if you are using some conditions to try and get the whole protein-protein or protein-ligand measurements and use ammonium acetate or whatever. It is less stressful when you've got one protein or a simple mixture, but it's a lot more stressful as your samples get more complicated.
The Sliding Windows part of SWARM is post-acquisition processing stuff. This is what threw me off. What if you assume that the adduct formation is a constant? Imagine you've got a single protein and you're going to incubate it with a ligand that will bind to it 1 or 2 or 3 or 4 times. You're already looking at intact mass spectra that aren't fun to figure out. Then imagine that you've got no adduct + adduct in there. Counting your no adduct no ligand protein you've got a 10 (?) actual protein combinations present and multiple charge states of each! Gross. Your deconvolution algorithm is going to have a hard time on this and every time it picks an adduct on accident -- fake mass generated....
In the simplest instance of SWARM (if I've got this right) you would run your protein alone, with no ligands. Then you'd figure out what is your protein and what your protein adducts are. Now you make the assumption that no matter if you add 1 or 4 ligands the level of the buffer adducts wouldn't change. So you subtract out all the peaks that have the + adduct signature! Yeah! I think this is what it's doing.
The authors demonstrate this works in simple cases then in more complicated cases, then backwards. The spectra are all acquired on a Waters QTOF with possibly an interesting nanospray ionization hack I'm unfamiliar with. (Could just be I haven't been around a Waters system in a loooong time). Deconvolution is handled mostly with UniDeC and SWARM is implemented through custom Python scripts. If they're publicly available, I missed the link in the paper.
I'm glad I continued to stare at this thing in sleepy puzzlement. There is a lot of power here, just not in my espresso this morning. I hope that the deconvolution software writing people take note of this. For something like antibody drug conjugates, this could be enormously valuable. The authors are careful to note that the main assumption (that adduct formation dynamics are consistent) may not hold true in all cases, but where it does? I'll take any decrease in spectral complexity I can get.