UniProt has a page and resources set up for 2019-nCoV now!

A lot of people downloaded my ugly FASTA for 2019-nCoV after I posted it. UniProt has done their normal crazy meticulous job of assembling all the data and is a much better resource.

You can check it all out here.

Peptide biomarkers for bacterial pathogens!

I've only got a few minutes, but -- wow -- is this ever worth reading!

Microbial ID by shotgun proteomics is NOT new. But promising study after promising study seems to end up with -- no new clinical assays.

MALDI-TOF with a BioTyper is easier in the clinic, I guess, but maybe we just need the right technologies to get us over the hump. Clearly, the insistence of researchers to continue utilizing NanoLC is a big hurdle, but maybe innovative sample prep methods would also help bridge the gap?

They use some crazy technology in this one. A flow cell digestion method that allows a tryptic digest of bacterial proteins in one hour? And a depletion technology that removes "host" (human!) biomass??

I have to mention that this study is a big collaboration between groups in Stockholm (where HUPO 2020 is!) and Gothenburg, a city blessed by some dark metal gods or something to be the birthplace of the greatest bands that have ever walked this earth. Yup, I definitely had to mention that.

22 Phosphoproteomics Data Analysis solutions go head to head!

Sometimes I take a dataset and compare 2 different data processing pipelines. One time, maybe I compared 3? 

22? What? Wow! Why do we even have 22 pipelines?  The abstract suggest that there are very good reasons, actually -- the results aren't the same....and they propose a solution for this. Only a paywall and a biological requirement for sleep stand in my way of reading this right now!

As a reminder -- there is a super epic community proteomics PTM challenge coming up in less than 2 weeks and I think maybe 10 labs have signed up for it so far.

I think that this is probably a great resource to help set the stage.

Covalent Protein Painting to measure in vivo protein misfolding!

If there is an easier looking experimental method to measure protein misfolding in vivo, I've never seen it.

If you are interested in structural proteomics stuff at all, I highly recommend this preprint.

Formaldehyde is pretty efficient at binding to proteins! Turns out that:

1) you can get heavy stable isotopically labeled formaldehyde
2) in your cells the formaldehyde can only get access to the outside of your protein 3D structures, effectively "painting" the surface of them.
3) You can compare different biological conditions by using "heavy" and "light" formaldehyde.

Digest your proteins with chymotrypsin and 'voila -- you can quantitatively compare the outside of your proteins and protein-protein complexes!

The downside here is that you have to think hard about the peptide identifications as -- CDH2 : 13CH3 , 13CH3 : CDH2 , 13CHD2 : CD3 , CD3 : 13CHD2 -- could correspond to Disaster Level: "deuterated deamidation" study.

To fully eliminate this an issue, these authors acquired MS/MS at 120,000 resolution! Which...in my opinion is overkill, but on the instrument they used, theyv'e got 60,000 or 120,000 to choose from and 60,000 is going to get a little sketchy on the larger fragment ions. (Loosely related...I commonly run at 90,000 resolution on another instrument...)

Despite the decreased number of scans possible on an LC time scale, they come back with a tremendous amount of data.

In case any of the author see this -- Unless I'm completely misunderstanding what I'm seeing -- Extended Data Figure #4 is possibly my favorite visualization I've seen of anything so far this year. (Maybe I should put this commend on the bioRXIV thing like I'm supposed to....)

Oh yeah! I almost forgot! On top of how cool the technique is, the authors make some interesting findings regarding protein folding and alzheimers!

Remember that Prosit thing everyone was talking about? It is super easy to use!

It's about time that we talked about how to add....

...well...deep learning...(but...come on, I HAD to use that when I found it, right?!?) to your proteomics workflow!

Don't want to read my rambling about why Prosit is awesome and just want to do it? Skip to Part 2 below!

I almost guarantee that there is someone at your facility who drops all sorts of words like this around -- and maybe that same person has given you reason to question their intelligence in other matters, but as long as they keep saying things about "neural networks" and "semi-supervised" whatevers it seems like everyone wants to talk to them, and maybe give them lots of money. Follow this easy walkthough and THAT COULD BE YOU. 

I jest, because Prosit is the real deal and has real world advantages, including more and higher confidence identifications right now.

For a biomolecule, the peptide bond is a joy to work with -- energetically -- crudely optimize the collision energy and you'll break most of them. Our friends in the small molecule world, where I continue to dabble don't have it anywhere near as good. There seems to be no rhyme or reason to what energy will break which bonds. When I do QE metabolomics, I step my CE, typically with 10, 30, 100. Just to come close. The ID-X even has something called "assisted" where it tries to help. Most of the time when you've got a molecule you really want to study, it makes sense to run it 10 times with different energies....

However -- just because peptides are better than most molecules at fragmenting, that doesn't make them consistent. Look at them. Why on earth would you miss the y7 in this peptide or the y4 in that one? It's just not there. And -- at some level it must make sense --energetically.

Prosit was described here last year:

In as few words as I appear capable of writing -- Prosit looks at the ProteomeTools database (you know that thing where they are synthesizing EVERY human peptide and then fragmenting them and making libraries?) and it models the peptides YOU give it against that library with this deep learning thingy.

PART 2: How to use Prosit! 

You will need:
1) A protein .FASTA database.
2) The EncyclopeDIA (you can get it here)
3) That's it. I just felt dumb making a list with 2 entries in it.

EncyclopeDIA can do all sorts of smart stuff (some of which I wrote not smart stuff about here) -- and it also has awesome utilities. Such as "Create Prosit CSV from FASTA"

As an aside, I heard from the Prosit team -- they'll have this integrated soon, but if you wanted to put the words "deep learning" on your ASMS abstract that is due tomorrow you have to do what I am doing.

This is ridiculously easy. Add your FASTA. It will make you a Prosit .CSV file. I believe very strongly in you and your abilities. You'll definitely be able to do it!

Now -- go to proteomicsdb.org/prosit and load that CSV you just made.

Hit next and then tell Prosit the format of your output library:

I'm using MSP because I can't afford Spectronaut yet. Then submit your job!

Now -- this is important. When you submit the job you'll go into the queue. You'll want to copy the link URL it gives you and/or the Task ID number. You will not want to close your browser without remembering to do this, because you won't get your library. When it's ready you'll get a download link!

If you want to check the quality of your MSP library -- the PDV is a nice, lightweight, java program that will allow you to flip through all of them. If you've already got the NIST MS Interpreter installed it will also load them. PDV will look something like this!

For this peptide, Prosit predicts that for a CE of 27 I'm not going to see every b/y ion. There are some bonds that it thinks, from the hundreds of thousands of real peptides it has studied, just won't fragment well.

And if, for example, you are looking at that real peptide. And it's right? Then you aren't penalized for missing that fragment when using this library!

Predicting PTMs in 2019-nCoV Wuhan Coronavirus

Yeah....maybe I need a hobby....but I think this stuff is cool AND I've learned how to use some new tools thanks to my curiosity about this new virus and thinking about how I would analyze proteomics data from the virus if I could get my hands on it....

Here is the question: PTMs don't typically just happen indiscriminately. There are particular motifs that are the targets of the enzymes that add the PTMs. So...can we start with just some unknown linear proteins and predict what PTMs that we would find?

And...are those predictions any good? I can't yet answer that part directly, but I'm trying.

There are a LOT of tools that predict PTM sites. After two late nights of trying a few of them and doing a lot of failing -- this older one is my current leading favorite -- and you can read about it here.

If you've got better things to do on a Saturday than read, I got you, yo! 

You can also just go and dump stuff into their server at ModPred.org. The interface is super straight-forward. Put in your protein FASTA entry (one at a time), pick your mods and hit the button. (You can also install it locally, but I'd rather use their electricity.)

You are capped at 5,000 amino acids per model with the web interface of their server.  And you are definitely penalized for longer sequences. At 1,000 amino acids, I recommend walking your dog.

Okay -- so only one protien from the 2019-nCoV translated FASTA is over the cap, so I broke it into 5 separate translated regions in order to have a large overalap in peptide sequences (in case the domains it is modeling against for PTM prediction are large ones). And -- it took basically all morning.

You get a pretty output that you can keep or have it kick you out a Tab(?) delimited text file. I spent a lot of time swearing while combining everything into a single Excel file (I need to grow up and stop using Excel. It always seems like it will be easier -- even though it increasingly is not the easiest solution.

Okay -- and here I'm talking smack about Excel -- and the Ideas button just did something smart!! Normally, it's just funny to hit the button, but -- darn -- it made a decent Pivot Table!

If you're interested in the actual motifs predicted to be modified, you can download them from my Google drive here.

Okay -- so -- that's all nice and all. Predicted PTMs are a pretty big step away from actual PTMs.

..and rightly so...

Can we test this?

I mentioned a couple of days ago that there was some cool unpublished MERS-CoV proteomics data on MASSIVE.

Now -- this is CID ion trap MS/MS data -- not my favorite source of data for identifying PTMs. It also kind of rules out some of my favorite tools, because they were designed with HRAM MS/MS data in mind. So...back in the time machine to the 1990s to fire up SeQuest and take a minute to polish up my sense of skepticism....

Okay -- this will take more than a minute or two....I forgot how long CID MS/MS takes to search with a couple of PTMs.

I broke it up into queues and only one has finished -- aaaaaaannnnnddddd....nothing!

Okay...so I do actually need another hobby....maybe something I can do inside, in case I screw up my knee and have to do a lot of sitting around for a while.

However -- there is A LOT wrong with this system. One -- we're looking at single shot analysis from 2009s best mass spectrometer -- in a human cell background. We're not exactly digging to the full depth of the proteome -- and PTMs rarely want to announce themselves. Two -- I'm using a prediction model of one virus that is similar to another, but we are definitely reaching when trying to make predictions off the little data across the board. Three through 41 --? I didn't even look to see if that region of the similar protein is even digested by trypsin. Maybe that is for next Saturday.

Posting some friendly reminder from Dr. Yates.

One of the laziest posts I've ever made...but I've got a lot of stuff to do this weekend.....

ASMS ABSTRACT DEADLINE IS MONDAY -- YES. FOR THE MEETING IN JUNE!!

PCR + Mass spectrometry for Coronavirus detection!

This study is a couple of years old, but it highlights a whole clever way of detecting pathogens  -- amplifying DNA and then doing mass spectrometry of the amplicons.

Advantages:
-You can start with virtually no DNA and make a ton of it
-MALDI-TOF is fast when you've got a ton of samples to screen
-MALDI instruments, while maybe not very common in research environments, are increasingly common in clinical labs. (Big question, though, is the flexibility -- I think that a lot of these are locked down to performing one specific assay, but -- still -- those places would have staff with the technical expertise to prep the samples and run the instruments
-If you pick primers well, you'd be resistint to mutations in the viral strains

Cons:
-PCR takes time. Is it faster than it was?
-It takes some people a long time to make primers and to verify they don't cross-react. (I've heard tools have gotten better)
-MALDI is almost always connected to low power TOF devices (sensitivity, resolution and accuracy are the things that are typically the low part)

Check out this alternative technique that would alleviate some of this --

Same general idea -- 15 years ago -- this group amplified their virus DNA (SARS-coronavirus) and then did FTICR....are there a few thousand smaller, faster FTMS devices around the world right now? If you've already done PCR would you even need to couple it to HPLC? FIA-MS on an Orbitrap?

Sorry if you're tired of hearing about these viruses, but I'm motivated to read/write about it

On the topic of the 2019-nCOV (Wuhan) coronavirus -- check out this beautiful resource from NextStrain!

If you are working on this from a proteomics/mass spec/clinical detection perspective and want to talk, please reach out (orsburn@vt.edu). I'd be happy to lend a hand developing/troubleshooting assays or by (much more usefully) connecting you to people who could be of great assistance.

And -- while I'm sleepy rambling -- check this out -- ModPred (www.modpred.org) thinks palmitoylation is a dominant PTM -- if you are developing peptide specific assays -- I'd skip that entire protein terminus.

MaxQuant.Live 1.2 is live!

I'm a little behind on this and don't have actual data to compare yet, but MaxQuant.Live 1.2 is up for download.

Important factor for many of us -- it still appears to rely on the same Foundation and Tune 2.9 (no 2020 mandatory upgrades if you were already using it in 2019).

The interface looks a little cleaner, but if  you were also hoping for some magical new data acquisition method to appear in the App store, you'd also be disappointed.

Given how active the developers have been on the awesome MaxQuant.Live Google group discussion forum, I think we're looking at a great software that still hasn't been utilized to it's potential, but has had some minor bugs ironed out.

APEX Proteomics applied to Stress Granules Provides insight into ALS progression!

A great way to see trends in proteomics is to go to ProteomeXchange and see what everyone is uploading.

The word "spatial" is completely blowing up. I think there are a couple versions of the modern "spatial" techniques. APEX, however, might be the best example. There is a great overview of this technique on the Krogan lab website that you can view here (I don't want to steal their nice image!)

Want the 6 18 second description/reminder?
1) You make a version of your protein of interest with a peroxidase on it and then you put it into your biological system and let it interact with all it's friends.
2) You put some biotin-phenols that float around in your system doing (presumably) no harm or alterations to your system
3) !!SURPRISE!! your system by adding hydrogen peroxide!
4) Nothing special happens to any other proteins (except normal H2O2 effects, I guess) but your protein of interest has that peroxidase on it --it reacts with the biotin phenols around it which causes all it's local protein friends to be labeled with biotin!
5) Pull down the biotin labeled proteins and now you know all the proteins in the general area of your protein of interest. Cool, right?

(It is a bit more complex than this. You don't want the APEX fusion being expressed all the time, for example, you can wait and activate it when your cells get ot the right stage -- you also need ot quench the reaction, but now I need to change how many seconds this took again...)

However, if you want to see it in action in a medical context -- check out this new preprint!

 
This group used multiple APEX Fusions to study stress granules (SG)s which according to the authors "form in response to a variety of cellular stresses by phase-separation of proteins associated with non-translating mRNAs" (yes, stolen from the first sentence of the abstract).

Unlike a lot of spatial labeling techniques -- this one requires nothing special from the mass spectrometer. The biotin labeled proteins are pulled down and digested. This group used a QE Plus for part of the study and it looks like they upgraded to a QE HF at some point. The LC separation was a long (3hour) gradient on 50cm columns. A relatively cycle time is maintained between the two instruments by using 17,500 resolution for MS/MS on the Plus and 30,000 resolution on the HF. The data processing here is MaxQuant and it looks very typical.

With this system set up, the experiment gets more complex, with the addition of ALS linked dipeptides that show alterations in the stress granules and the SUMoylation -- which is where the biology goes beyond me.

What I do get:
1) A fantastic application of this powerful new technique
2) A method that demonstrates that I could definitely do my part of this.
3) Important new understandings into the progression of a protein disease?