Simple (& surprising?) strategies to find more PTMs on quadrupole Orbitraps

I'm at a loss here. Time for more espresso? The observations in this new study don't gel with my mental framework, but I think this is definitely worth checking out, because these authors build a solid case for their observations.

The first part here is easy. These authors demonstrate the use of static inclusion lists as being very useful for finding the acetylated peptides that they are interested in. On their quadrupole-Orbitrap they can put in up to 5,000 target ions for fragmentation and aside from that they run it as a normal dd-MS2 experiment. If the instrument doesn't see anything from the target list in the MS1 scan it goes ahead and fragments the most intense (presumably, that pass the "peptide match" protocol selected). Somebody had a really catchy name for this, I forget what it was -- gas phase enrichment or something? 

EDIT: Since I'm already here 'cause I misspelled "Orbitrap" in the post title..ugh...something else I forgot. They mention that other systems can accept 50,000 targets in their inclusion lists! I've always wondered what the upper limits were.

WAIT! I get it now. There's hope for you yet, brain!

The part I couldn't figure out was this observation -- that turning off "exclude isotopes" increased the number of acetylated peptides that they identify. They enrich acetylated peptides from these organisms and the resulting mixture is relatively simple. By turning "exclude isotopes" off, they are  allowing multiple fragmentation events to occur for each acetylated peptide, essentially getting around the dynamic exclusion settings! Presumably they have plenty of cycle time to get to each acetylated peptide multiple times and increasing the number of times the peptide is fragmented increases the chance of positive identification (looks like all IDs are with Mascot, btw).

They find that just turning off that feature allows each peptide their interested in to be fragmented 4-7 times more than when they leave the feature on and this massively increased their chances of accurately identifying the modified peptides.

Honestly, in this same circumstance, I'd probably have started with crudely matching my peak width with my dynamic exclusion windows. (Some of my not-as-terrible ramblings on dynamic exclusion optimization can be found here and here and here.), but their strategy appears to work quite well in their hands and you don't have to go messing around estimating peak widths.

A final interesting note in this papers is that they stress the importance of manual interpretation of MS/MS spectra for modified peptides. I agree 100% -- until I realize the dataset in front of me has over 1e6 matched PSMs....then...

I mean...I definitely check the important ones!  (Not to sound like a slacker, but if you spend 1 second on each of 1e6 spectra, that is over 6 full 40 hour work weeks...and on a modified peptide I need a whole lot more than 1 second...)

There are more interesting observations in this nice study as well. Definitely worth checking out!