"Proteins don't exist" - if you don't know the proteoform, does it even matter?

Shoutout to Jens for forwarding me this cool C&EN article I hadn't seen. We had an offer to go to iHUPO, but -wow- it was far this year for me. It's far closer in 2027 (MILAN???) and 2028 (RIO!!) which would have been scary but they exported their fascist would-be dictator to my country where we collect and empower them. 

As fast as proteomics is gaining ground in public consciousness, it's tough to miss a conference this big and especially when it looks like there were smart people stating smart things.

Do you actually care what the relative concentration of protein A is? Or do you care about how much of the active proteoform of protein A is actually there? 

Combinatorial peptide tags to find peptides with multiple modifications!

As much as it's fun to hate on typing things into command line or console - this doesn't look too bad 

./MSAndrea -s spectrumFile -d proteinDatabase -e settings.xml [-o outputfilename]

I might honestly be about to try it on this MacIntosh thing so I can run this - 

Which you can get here if you don't want to read stuff

There are a lot of ways to get a delta mass from an MS/MS spectrum. Fragpipe, SAGE, just putting a ridiculously high mass error into the precursor mass of just about any software and waiting a really really really really really really really long time for it to finish processing.

Some of these can assign you a solid match for what that delta mass might be. +15.999? That's probably one of those oxidation things. 

What MSAndrea does is a series of filters and reassignments based on what it finds inside of the MS/MS spectra to try and figure out the hard ones. This is particularly improtant for some of those real life biological problems where the difference between an acetylation on site 1 being an active protein, an acetylation on site 2 in that peptide being off, site 3 + site 1 being HOLY SHIT EVERYTHING IN THE NUCLEUS IS BAD, site 2 + site 3 being NEVERMIND EVERYTHING IS OKAY and site 1 plus site 4 is something no one ever saw before until today, so you'd better be really really sure it's site 1 and site 4 both acetylated before you give that Excel sheet back to that lady with the HHMI or Nobel or whatever. 

Notably, I'm not sure that this nice new study on a cool new tool I just downloaded is ever compared to MetaMorpheus which is my go-to for PTM hunting, but that's probably what I'll do next. 

Notably MS-Andrea has suggestions for search settings for a bunch of instruments. Actually, that's useful here I think. 

Making the possible impossible: Small window DIA TMTPro!

Why does this look like I took the screenshot with a potato? Meh? 

Okay, so this has been attempted before, but this is probably the first time it's really worked. 

Here is the question: Why wouldn't you just do isobaric tagging (TMT) with little tiny DIA windows? We know from things like TomaHaq that you can do targeted TMT quan and it can work well. So why wouldn't little DIA windows be better than data dependent acquisition for TMT? The biggest problem with TMT stuff (aside from coisolation interference) is stochastic sampling. If you miss fragmenting peptide A in run number 2 you don't just lose that peptide in one sample - you can lose that peptide in 18 samples! 

If you did little DIA windows across the whole mass range? Problem solved. 

New problem?

It's super duper slow. Man, I'm on a roll. I don't think that's even moving? Meh. 

But with these new fast TOFs could you do it? 

It doesn't look bad at all. Loads of optimization stuff here. Because if you fill for longer then you get more signal but you get more coisolated peptides. Totally worth checking out! 

ProFormer - is this the smartest metabolomics tool ever - designed for proteomics data?!?

 

I should spend more time on this one than I have, but I've forwarded it to smarter people who will understand the words. 

What I think is happening in ProFormer - what if you took a step back from proteomics data and just looked at the MS1 intensities and used those for clustering and stuff? That's how a lot of untargeted metabolomics stuff starts - at least for the QC steps. And then...it gets very manual from there. 

Toss it all out! Let's ProFormer! 

Cause what if we went beyond the QCs for the MS1 signals and we used that to understand the samples themselves? Here they use deep learning magic stuff on those MS1s and show they can start classifying proteomics samples? People do that for loads of transcript level measurements even when they have signal to noise ratios of 3:1 in individual files. (Try getting anyone in GWAS to give you even an estimate on what the S:N of those measurements are. Come on, it has to have something equivalent, right?) 

Where ProFormer stands out to me is 1) yes, this is super smart for proteomics and single cell proteomics, but would it be just as powerful as a metabolomics tool?  Super impressed. Hopefully it doesn't need a Docky and a thing you just type letters and numbers into.... 

CasanovaGUI - next generation de novo sequencing for the rest of us!

 

Hey you! Do you want to denovo sequence some files? Yeah! 

Do you want to put a docky on your graphics card and run a bunch of programs by typing long things into something called a commandy promptier. Yeah. Me niether. 

And now you can! CasanovaGUI runs on Mac, Linux and Woondoows.

Great new book alert! Bioinformatics for Mass Spectrometry!

No way I'll get time to go through all of this soon, but highlights include 2 very good chapters I have spent time on that focus on PTM site occupancy and normalization.

rRoma looks like a very nice tool with a clean and organized Github and the other one is about strategies. Not a bad start! And there is a chapter on boring and important metadata stuff! 

Faster mass spectrometry = better metaproteomics differentiation power!

 This great new paper in JASMS shows what this generation of mass spectrometers can do for the most complex of samples! 

Bonus! Did you know that Asstral actually stands for something? This is where I learned it. 

Mix a bunch of different microbe communities together and what do you get? With hundreds of scans/second you get a lot more of those little peptides that can really differentiate bacteria A from bacteria B. This is important because when you're looking at HSPs or ribosomes that are super evolutionarly conserved the peptides that are the same among all the organisms are multiplicative in each sample. And also not useful for telling them apart! Great argument for TOFs in metaproteomics applications for sure. 

fracSLIM - the next step toward the inevitable death of nanoflow in proteomics?

 

If you've ever been here before 1) I'm sorry and 2) you probably have seen me make fun of aptamers a lot and you know I despise nanoflow chromatography. Is it a necessary evil? It is in single cell proteomics for at least a bit longer. Otherwise there are other options, like microflow or even analytical flow. 

What about high flow fractionation and then direct infusion SLIM (high resolution ion mobility) + Orbitrap (high resolution mass spectrometry)? Could that work? Maybe it's worth taking a look at this new paper! 

...and in next week's lab, we'll build a mass spectrometer to use the rest of the semester....

11.1% of students said that BUILDING THE MASS SPECTROMETER DIDN'T HELP THEM UNDERSTAND MASS SPECTROMETRY! 

I WANT TO TAKE THIS CLASS! 

Where do proteomics people come from (and why aren't there enough of us)?

 

Wow, this thing has gotten all sorts of positive comments (thank you! reviewers and readers!) and I guess I forgot to post it here. 

Background: Right after the pandemic there was SO much proteomics excitement. (Reminder we had a huge Forbes article with a picture of something from a museum on the cover. WTF is that thing even?)

And there seemed to be a pile of new money for a while right after that. A lot of it ended up used up by confident people who had no idea what they were doing (not a surprise, that's how all science business stuff seems to work) and decided it was time to enter the proteomics space.  I know of multiple situations where people with no proteomics experience - and with none on their teams - successfully scored investors, bought instruments and.....are having slow starts...because they either had to learn how to do proteomics in a startup pressure cooker (fun) or had trouble finding experienced operators, because there aren't lots of us. 

But here is the question, though. Why ARE there so few of us? And that's the reason behind my most recent paper.  I figured if we knew where people were coming from (even from a small sample size like the people who will respond to social media surveys [thank you!!]) we could start working on strategies to fix the problem.