Saturday, September 30, 2017

Unrestricted data analysis of protein oxidation!

Okay -- you're gonna have to trust me on this one -- this figure above is actually really cool, but I can't get even the single image to copy over here right. I even tried (on purpose!) to open this paper in the "Active View" thing...

It's from this paper that is way too smart for me this morning.

In general we still have to limit the PTMs we go after in a study. Maybe that's going to change soon with some of the next generation algorithms that are coming, but right now we need to be restrictive. People studying protein oxidation in a biological context -- for example in aging research -- tend to focus primarily on carbonylations. We know from induced oxidation studies, like FPOP (which is probably an extreme example) that oxidation can have all sorts of different effects on a protein.

What this team shows here is a somewhat counter-intuitive way of looking at all sorts of oxidative events, even in complex matrices -- as far as I can tell, by just using MaxQuant in a clever way and some relatively simple post search filtering.

All the data they show is from a Q Exactive with 70,000 resolution and 35,000 resolution MS/MS. I think the resolution in the MS/MS is pretty critical for what they are doing. Even though mass accuracy doesn't really change with increased or decreased Orbitrap resolution, their downstream filtering is super harsh and co- corresponding fragment ions at lower resolution will probably lead to a real PTM getting tossed.

If you're trying to resolve a modification of tryptophan chlorination (+33.96) from homocysteic acid (+33.97) you might want to double that resolution ( does help a little that this example occurs on different amino acids... ;)

Something that ends up being ultra-critical for them is the "dependent peptide search" function in MaxQuant. Fabian Coscia describes this function in this YouTube video here (description of the function starts at 9:19, but the whole thing is worth watching.)  This slide screenshot does a good job of summarizing how it works.

These authors utilize this function and then export the resulting delta M peptide modifications and filter them down to known oxidative modifications (oh -- their samples are treated with something that oxidates the Albert Heck out of them.)  What they find in a very simple mixture reflects in a much more complicated sample -- specific oxidation "hot spots" and a whole lot more interesting protein oxidative modifications than carbonylation! Once they find them -- they've got MS1 signal to quantify them with.

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