Cool TGen new mass spec video!

This video is pretty fun!

UniprotKB improved its interface for helping us find references!

This might sound minor, but its going to make some of my days easier!

If you land on a cool protein that is differentially regulated in your organism in UniprotKB, they've added a handy little button (highlighted above) that just says "Publications"

And it actually pops up publications. AND it doesn't just pull up articles where your protein name appears in the title!!! It pulls up big studies as well where your protein was listed!

Sometimes its the little things! (Shoutout to @PastelBio for pointing this out)

Cysteine modifications in aging and neurodegeneration?!!?!?

Who was I talking to the other day at length about cysteine modifications? Somebody locally here in Maryland. I have the vague impression I felt outclassed intellectually, but that doesn't really narrow it down too much...

Eons ago, I was involved in a study where normal shotgun proteomics techniques totally messed up our work. We were studying a compound and it caused big protein mass shifts if you incubated it with a single or a mix of proteins, but we couldn't find peptide mass shifts. Turned out that the compound loosely bound to cysteines and when we reduced and alkylated the iodoacetamide displaced the compound. (We weren't reducing and alkylating the intact proteins; just shooting them intact). I've always wondered since then if we are losing other information about cysteines by using these techniques.

(Side note: it is a very common practice in big Pharma companies I've visited that are studying antibodies for them to do a digest of their antibody with and without reducing and alkylating and most of their software can take both runs and make conclusions about what the cysteines are doing)

Want to worry more about cysteine states?  Check out the study in the screenshot above (link here)!  Maybe this is all stuff you already know that I don't, but this review matter-of-factedly states all sorts of stuff about cysteine PTMs that I knew nothing about until this morning.

It is well established that cysteine oxidation is linked to aging (what?!?) cause the redox stuff that cysteine does that is critical to many protein functions is inhibited by the build up of modifications on it (??).  But not very much is known about what specific PTMs or patterns of PTMs are the most critical.

I DON'T EVEN LOOK FOR CYSTEINE PTMS!  And they talk about 8!!! reversible cysteine PTMs that play really key roles in cellular regulation and stuff that I probably can't see cause I've blasted all my cysteines with a crazy strong reducing agent and then bound something to them.

However, all is not lost --- this paper discusses the established techniques for going after these PTMs. By using alternative reduction techniques or even modifying the PTMs themselves, you can study the changes in these -- and even, in some cases, directly enrich for peptides with this cysteine modification state.

Each one appears highly involved, but if you are sitting there looking at a phenotype that you can't explain from a global proteome level, maybe this is something to go after?  I doubt you could find a more definitive review on this topic!


 

GAPP -- Proteogenomics and PTMs for microorganisms!

...um...this statue is in DC...and I'm pretty sure you aren't allowed to climb on it. But it is my favorite image that popped up when I went for "on the shoulders of giants" cause this is what GAPP reminds me of.  Watch for the park police, kid!

So...what if you looked at the open source toolkit landscape for proteomics (and had programming capabilities!)? Would it make sense to completely build your own tools right now? I totally wouldn't!  There is so much cool stuff out there, I'd just piggyback some existing framework and add what I needed.

And this is EXACTLY what GAPP is doing.

You can read about it (in press and currently open at MCP!) here.

Check out what is inside the Zip file if you get it from SourceForge here!

I was a little alarmed by how long it took Windows Defender to scan through this file (it was clean, but always scan whatever you download!) but it made sense when I opened it.

GAPP fills the gaps all our favorite tools leave in the Microbial proteogenomics workflow. They couldn't find anything that would make their database -- search their proteomics data (extensively! look at all those engines) and process their PTM data, so they made a Java interface that would by using a bunch of tools that are already out there -- awesome and can be networked together.

This is the general scheme:

To validate that the complicated thing all works -- they downloaded a very nice and extensive H.pylori dataset from PRIDE (this one from Muller et al.,) and went to work.

Worth noting:
 There are some pre-requisites necessary to install this. You must have Java 1.6 or later and you must have Perl already installed to use the Java interface. I can't tell for sure, but I'm going to suspect that you are going to need the MSFileReader separately installed as well, as its a pre-requisite for the ProteoWizard.

I really like this study. We don't need to reinvent the wheel every time!  Sometimes we can just tweak the awesome stuff that is already out there!

How does the Precursor Ion Area Detector node work?

I'm procrastinating this morning and just when I was running out of excuses for not finishing an ongoing bathroom remodel, I realized there were a bunch of unapproved questions/comments on the blog!  This is the last one. After writing far too many lines in the little comment box about why the NIST antibody is so much better than the commercial sources that have been around, I didn't want to tackle this one the same way.

How does the Precursor Ion Area Detector node work? And a reference?

The reference might surprise you!

You can direct link to it here, and I think its open access.  Look, I'm gonna give Q-TOFs a hard time. I've only had one in all my career and it was, on its best day, a turd sandwich not very good. [Completely dedacted rant about Ben's hatred for Q-TOFs and sarcastic statements about their many uses as well as recently acquired facts regarding their value in scrap metal components].  Wow, I feel much better about publishing this post now -- and it is much much shorter!

Remember, though, that there was a day when this was the cutting edge and people were just as smart back then as they are now, and they did good research despite the limits of their instrumentation!  This paper is such a study. It is definitely intended to be a paper showing off a new (at the time) fragmentation technology, but in it they set the framework that most label free quantification is based on --or at least, influenced by.

The idea -- the high resolution (here 10,000) extraction of the intensity of the 3 most intense peptides from each separate maximum intensity is very strongly correlated with the abundance of the protein.

This is the Proteome Discoverer interpretation  -- you're ticking along and identifying peptides and you assign each PSM (peptide spectral match) the intensity it had in the MS1 event that it was selected from.  When you compile the PSMs into the peptide, if there are more than one PSM the peptide is assigned the intensity of the highest PSM. When the peptides are pulled into the protein or protein group, the average of the intensity of the (up to) 3 (adjustable in PD 2.1) peptides is averaged into the protein area.

If you have a protein that has only one PSM, this is easy. The "area" of that protein is the intensity of the PSM.
If you have 3 PSMs that all go to one peptide and into one protein, still easy. The "area" of the protein is the intensity of the most intense PSM.
If you have 3 PSMs for each of 3 peptides, the protein "area" will be the average of the most intense PSM from each peptide.

Important note here!  The protein "areas" will not always be calculated from the same peptides. If you've got something where you had 50-60% sequence coverage and have 200PSMS, chances are it won't be the same peptides at all. But, seriously, this totally works at the protein level. You are going to need to go to the PSM or peptide level intensities if you want to say, for example, how this modified peptide changes from run to run, and that requires a good bit extra work.

Michael Bereman, who knows a little something about protein quantification (SProCop! and QCMyLCMS.com) and he told me it worked, if I remember correctly, "surprisingly well". I use it in virtually every sample I process in PD. It has never once hurt me to have that extra information!

Are there better ways of getting relative quantification of proteins and peptides? Sure!  And these algorithms are coming -- and are going to absolutely change EVERYTHING about how we do proteomics -- Minora, PeakJuggler, and IonStar are all getting ready for prime time and are going to usher in something I think will finally be worthy of the title "next gen" proteomics by allowing us to finally see all the stuff in Orbitrap data that we've never seen before. Your Orbitrap, right now, is far better than you think it is.

iMixPro -- less false discoveries in pulldowns with heavy peptides!

Affinity purifications (or much cooler...affinity enrichments!) are ever in increasing demand. What protein interacts with my other proteins and how may be one of the most important things we'll be contributing to biology in the future -- once its not completely fracking impossible to do it. The new crosslinking methodologies that are coming are going to help, but iMixPro is another elegant approach.

It is described in this awesome new JPR paper from Sven Eyckerman et al.,!  I'll start off by saying it isn't the simplest method you've ever seen, but if you've spent much time doing protein-protein interaction assays you've either developed your own complex methodology that works and you're keeping it secret from the world -- or you'd try just about anything to figure out what is real and what is not! -- especially when today's super sensitive instrumentation is telling you you pulled down 1,000 proteins with that expensive "specific" antibody you just got in!

It differs from affinity enrichments in that it intelligently employs heavy labels (this is where the "i" comes from in the name). Having essentially SILAC pairs to look at in their data improves even the label free quan approach you have in affinity enrichments. Combining labeled peptides = less batch effects, which is never a bad thing.  They show some great examples where they can remove the noise and find their true interactors, even when the intensity of the true signal is only a fraction of the value of the other signals identified!

New cool stuff in Q Exactive Tune 2.7

I popped by to visit Dr. Kowalak the other day to see what cutting edge science the NIMH Proteomics Center is doing these days and he showed me that his QE HF Tune doesn't look like my QE HF tune....

So I upgraded my QE HF Tune to 2.7SP1 to check it out!  There is a bunch of cool stuff in here!  One highlight: The confusing %underfill ratio is now gone and replaced by a much more sensible measurement. You now have a "minimum AGC Target" as well as your normal AGC target.

According to the manual, "IF the mass peak of interest reaches this minimum AGC target within the maximum injection time, a data dependent scan will be initiated"

I like this much better!

If I've got it right, this is the current instrument logic --

--and a pretty good drawing of me and my dog, Gustopheles (you're welcome!)

What else is included in the Tune 2.7SP1?

Loads of upgrades for the QE Focus (extended mass range, more MSX counts && some combination scans, like MS1 and PRM in the same experiment!)! And a software modification to make instrument bakeouts on all Exactives and Q Exactives more efficient!

Please remember that this is my interpretation and may not be 100% factually accurate or well drawn. Sometimes I put things like this up and the vendor involved will contact me to tell me I'm wrong and I'll walk away learning something. If that happens, I'll be sure to edit this later!

The best part of this is that I don't ever have to explain %underfill ratio again!

Threonine and Isothreonine have different HCD fragmentation patterns!

Yeah...I totally stole this from another blog in-between meetings, but its seriously cool. The original blogger put it up on Accelerating Proteomics here.

The original article is from KG Kuznetsova et al., and can be found here.  (Side note: Man, there has been some cool original research coming out of Moscow lately! Keep it coming!)

Wait. What is Isothreonine again? Well, its also called homoserine and we sometimes see it in proteomics data, but it generally isn't a good thing.

Check out this quick image I borrowed from Alexey Chernobrovkin et al., from this paper a couple years ago:

In this illustration, the protein is yanked out and digested and...crud...overheating the protein with iodoacetamide converts some of the methionines to isothreonines. Gross. Then, cause you don't have IsoThreonine in your FASTA, you end up finding a peptide with regular old Threonine in it. 

Boom. False discovery. Where is that a big deal?

1) De novo sequencing (nuts) -- you totally got a peptide wrong
2) Proteogenomics -- cause your huge database has lots and lots of possibilities in it. And...well...the chances that you'll have a peptide sequence in your database with a  xxxTxxxK (from a peptide that really started out as xxxMxxxK...but isn't there anymore is higher than when you are using a smaller, manually curated FASTA and your odds of making that mismatch is made higher just algebraically.

All is not lost, researchers who are banking hard on proteogenomics/metagenomics being the future!

Cause the original paper I found at the top did a focused study with synthetic peptides and found 1) the Isothreonine peptides elute differently AND there is a change in the HCD fragmentation patterns (actually the second paper I mention reports that as well), but they suggest that it would be reasonably easy to integrate this shift in fragmentation patterns into most proteogenomic pipelines!

GlycoPep MasList -- automatically build targeted lists for glycopeptides!

Shoutout to @ScientistSaba for helping me keep up with all the awesome stuff happening #HUPO2016 and still having time to tip me off to some cool papers like this one!!

The paper introduces this little program GlycoPep MassList.

The concept is simple AND powerful!  Feed it your protein and it will generate you an inclusion list for the glycopeptides that could occur given your parameters.

They demonstrate that it works on their Orbitrap Velos Pro. You can use it for a purely targeted experiment or within a "gas phase enrichment" strategy (like the "include others" button on a Q Exactive)!

MCP wants your opinions on targeted proteomic publishing guidelines!

MCP has always had (famously strict!) guidelines for what and how they will accept global proteomics data. They are now working on a draft for targeted proteomics data and have opened that draft up to the community for contributions.

Want to shape how we publish? Check it out here!!