This is really really cool and currently in press (open access!) at MCP and comes from work done at Roman Zubarev's lab. Edit: Here is the link to the abstract (left it out before).
In a DDA experiment we pick the ion we're interested in that looks like a peptide, based on the parameters we provide that say "this is a peptide and it is probably one that will fragment well with the method that I'm using right now". Then we isolate it and, too often, a bunch of
For years I've heard people kicking around this idea: What if we identify our peptide from our MS/MS spectra and then we remove every MS/MS fragment that can possibly be linked to that peptide. Then we're left over with the fragments from the peptides we accidentally isolated. Lets then database search that and find out what that is.
And that is exactly what DeMix does.
Let me rant a little bit about how cool the workflow here is. They ran this stuff on a QE with 70k MS1 and 17.5k MS2 and used isolation widths of 1 - 4 Da. They converted everything over to centroid using TOPP (btw, they found better results when they used the high res conversion option for this data, so I'm using that from now on. Next they ran their results through Morpheus using a 20ppm window and a modified scoring algorithm. The high scoring MS/MS fragments were used to recalibrate the MS/MS spectra (just like the Mechtler lab PSMR does) using a Pyteomics Python script.
Interestingly, when they made their second pass runs they tightened all of their tolerances and processed the deconvoluted MS/MS fragmentation events where the previously matched fragments were ignored. I should probably finish my coffee and then work my way through the discussion, because I would have done it the opposite way (and, when we do serial searches in PD, that is the default workflow). I'm not knocking it, I just find it counter-intuitive.
So what. Did it work? Of course it did, or it wouldn't have made it into MCP! Final stats? The QE was knocking out about 7 MS/MS events for every MS1. Using this approach, they IDENTIFIED 9 PSMS(!!!) out of each 7 spectra. They didn't get 2 ideas per MS/MS event, but they got about 1.2 which is a heck of a lot better than 1!
I can not wait to try this and I've got the perfect data set to run it on sitting right in front of me. I'll let y'all know how it goes.