SynGO -- A beautiful resource for neurons and synapses and stuff!

There are resources and databases for so many things popping up that it's impossible to keep track. This new one SYNGO is something I would have found really helpful while constructing a manuscript last year (I looked everywhere for something like this!) But it's in perfect time to help a new collaborator at UVA.

Studying synapses? This compiles data from the best studies and makes it push button level simple. You can even parse your data to only compare your results against the results that are not proteomics (in case accuracy isn't your primary focus in studying tiny bits of the ends of brain cells).

You can read about Syngo here!

Transcript Abundance IS NOT THE SAME as protein abundance!

It's 2019....and I'm shocked that this even needs to be said to anyone, but it does. I know it doesn't have to be said to the brilliant people who, for some reason, come to this blog to read my rambling about proteomics, but -- hey -- maybe my annoyed rambling here will actually be useful to someone!

Here we go:

Transcript abundance does not correlate with protein abundance.

I'm going to throw in proof that I filtered on one criteria -- "does it say it in the title or abstract?" because the people you're going to need to say this to probably aren't reeeaaaal into reading. Heck, I'll even highlight it.

#1 It can, if you go on a gene by gene basis (and organism by organism) and throw in adjustment factors.

#2 --- Maybe this is a new finding?  HINT: IT ISN'T!!!

In 2009, these authors suggest that you basically keep a list (it's going to be small) of the proteins where RNA abundance and protein abundance correlate. THEN you can trust the mRNA levels to be helpful for predicting protein abundance.

#3) Who's heard of these journals? Let's look at a really thoughtful review in something called Call? Sell?

I'm not even trying, yo. For real. I did a SILAC experiment (the gold standard for protein quantification) and RNA Microarrays on the same samples way way way back in the day. 1% overlap. Yes -- on an Orbitrap XL with SCX fractionation, I didn't get close to complete proteome coverage. I was PUMPED by a few thousand with quan. And microarrays died out for a very good reason (or should have, if they haven't).

1% overlap in quan. Yeah...my system was messed up...but, come one. You'd think by chance it would be better.

Great quality Youtube video -- XlinkX driven XL-MS studies

The XlinkX nodes that can be demo'ed/purchased and ran in Proteome Discoverer 2.3/2.3 (I need to look to see if I have them in 2.4...which I don't have on the PC I'm typing this on) can seem to be a bit of a black box. It's even more apparent when you're troubleshooting or trying to do something a little different than following XlinkX example set verbatim.

If that video box above was added correctly, you should be able to watch a really nice video by Dr. Richard Scheltema walk you through the entire workflow as well as how XlinkX works.

(There is still stand-alone XlinkX, btw, and I think it's still free.)

If the video link above didn't work, it can be watched directly on YouTube here.

Great tutorials on FDR and Parsimony!

It is completely possible to prep a sample, run an instrument and process your data without ever knowing at all how any of it works. And that's fantastic -- until something goes wrong.

On the data processing side so many things are just assumed based on 15 or 20 years? of work developing this stuff. If you'd like an exceptionally clear walkthrough on two of the harder principles -- false discovery rate estimation (FDR) and how parsimony (what to do with peptide identifications that are not mappable directly to a single ID -- here are two great ones courtesy of Dr. Phil Wilmarth.

#1 -- Shotgun proteomics and FDR

#2 -- Parsimony (and maybe a better idea than parsimony?)

There are other great things at the GitHub as well. 100% recommended and added to the "Resources for NewBies section over there. --->

Proteome + Metabolome + Immunome + Microbiome + Transcriptome Integration = Pregnancy Multiomics!

....this new manuscript might have the answers to a lot of the questions I think we've all been hearing, primarily....

"HOW DO YOU INTEGRATE ALL THIS "-OMICS" DATA!!?!?!"

I'm going with "might have the answers" because there are a lot of assumptions made by the authors regarding the math background of the reader.

When you get to the methods section you get this brief "how we did the proteomics, metabolomics, cyTOF, etc., etc., is all in the Supplemental" and this is the first description of the integration of the data...

....right on....

So...all the stuff I'm interested in is in the Supplemental.

The plasma proteomics is done by SomaScan. This is a bead-based array technology that is coming up fast. This one can quantify around 300 proteins per sample. I think we're going to see it continue to put pressure on LCMS proteomics for a couple of reasons.

1) Biologists are still doing microarrays (for real, they still are) and GWAS. They've got all sorts of ways to deal with data that isn't the most precise thing in the world.
2) Oh -- and we still have this reproducibility issue because none of us can agree on a single sample prep method for anything at all, ever.

 I really really hope to see a head to head soon to see what the precision/accuracy of this technique is versus someone who is good at proteomics. If anyone sees this, please let me know!

Even at 300 proteins this is still a ton of data across 50+ patient samples and I'm cool with this.

The microbiome stuff was done by a PCR amplification of the 16sRNA and the metabolomics was QE/QE Plus with one running HILIC and the other reversed phase.

The immunomics might be the highlight of the study!

Whole blood from each patient was aliquoted out and either not stimulted or stimulated with LPS or IL-2, and so on -- and then cyTOF time! For real, I've wondered what the heck you would do with these things (and so have other people, apparently, considering the number I've seen sitting around doing nothing after they've been purchased). If you aren't familiar, you essentially put a metal tag on an antibody and then the antibodies bind to the cells and you vaporize everything -- I think its inductively coupled plasma, but don't quote me --- then you use the lowest resolution mass spectrometer ever constructed (this home made one made with a spoon might be lower resolution, to be honest). You don't care, though, because metals differ a lot by mass. (It does limit the total number of antibodies you can use, but it's still a super cool concept.)

They get a signal, simultaneously, for every cell that comes through their cell sorter thing and gets vaporized, that can provide a concentration of ALL OF THOSE TARGETS! Pretty great, right?

If you'd like to look at the data yourself, it has all been converted to csv and integrated into R. All the scripts are available in a zip file at the very bottom of this page (it says the word "here" in a slightly different color).

Did I learn how to integrate multiomics data? Hmmm.....I've got a bunch of math to brush up on that might get me closer than I was before -- and -- well...I could just use all their scripts and put in my data....so...maybe!

MotifeR -- Better than just funny letters that are the wrong size!

I don't know about you but I've been very disappointed every time I've used any kind of a protein motif software tool....awesome...now...I've got a bunch of letters that are funny sizes and colors.

MotifeR may be what I've been hoping this motif stuff would do!

There is a cool online web portal (looks like a Shiny interface) and a full package for you smart people who don't want limits on what you can do.

The authors point out some advantages of their tool over other ones out there. One is kinda funny, because it's like "well....the server for this competitor went down in January and we checked back periodically over the last 6 months and...yeah...still down...."(I get it, maintaining online resources is hard! 

Other advantages? Directly links to UniProt FASTA for seamless downloads!

Has a walkthrough for loading your data from various output with both MaxQuant and SpectroNaut described!

And the vector plots definitely make it seem more powerful than funny sized and colored letters! Yo, it's definitely worth a shot, right?

I wonder how many mass spectrometers are sold this way....?

....does this deserve an extra post? Probably not, but I was on a PC without adblockers enabled and -- an ad suggested in 3 images why I should drop...I dunno....about a million bucks on the newest mass spec....I mean...I wasn't in the market.....but....

Current understanding (and challenges) in Human Metaproteomics!

What's all this talk of metaproteomics anyway?

How does it play in with "microbiome" buzzword everyone keeps rambling about all over the place?

This may be the ultimate guide to where we are right now in applying proteomics (and genomic) technologies to understanding the micobial community (microbiome) in the human body.

We might be, by mass, mostly one organism but we're vastly outnumbered by the organisms that cohabitate our space with us. (I attended a talk a few years ago, and I wish I knew who gave it, but the speaker told us to think of ourselves as just the vehicle that the microbes use to get around and multiply -- which I don't suggest, because it's gross....)

I like this guide because it shows the places where the nucleotide based technologies are ahead of the protein based ones and it is quick to point out both the powers and the challenges that we have ahead in really figuring out the microbiome and using it to improve human health, which might be my new record for longest sentence I've ever typed, particularly once you take this segway into account...now.....BOOM! new record for sure!

New Universe of miniPROTEINS is upending cell biology!

If you haven't seen this new editorial in Science, you should 100% check it out.

MINIProteins? You mean the ones that all of us could do and feel like experts in TOP DOWN PROTEOMICS?!?!  Sign me up!

50 amino acids? That's 5-6kDa.

1) That'll separate nicely on C-18
2) I probably just have to run it through a MW cutoff filter!
3) I'll easily get baseline resolution of the MS1 at probably even 60,000 resolution?
4) 30,000 resolution would probably be all you'd need to get charge states and deconvolute the fragments? No microscans, no stepped collision energies? Realistic cycle times could explore this new mini universe of important little proteins!

If you do go after these things using digestion and shotgun proteomics, please take a look at how your software is doing "protein grouping". In the newer versions of Proteome Discoverer, for example, the larger protein sequence gets the top billing in the group when evidence is even. (For you PD 1.4 holdouts out there, it's the opposite for you). Another reason we should be doing more top down -- and Science says there is some low hanging mini-fruit out there!

Mandatory according to blog rules:

Quantitative proteomics of cysteine activation in T-cells!

Add another huge set of techniques to the list of things I had NO IDEA that we could do or were important, and now I do to a great new preprint!

Where to start? Let's go first sentence.

...so...what's going on with the cysteines and what is stuck to them is some critically important thing. I knew part of that. I studied some drug a million years ago that was supposed to be a PARP inhibitor but just generically bound to every cysteine it could find. I thought that it was just screwing up protein 3D structure or something. Of course -- cysteines are way more important than that.

"...PROFOUND EFFECTS...!?!?!?" I just heat my proteins up with DTT and then put iodoacetamide on it. Guess I can't go back to my old data and see evidence of these profound effects....and I'd guess I can't use that awesome MASSIVE tool to search other people's data to look for it either....cause I betcha that DTT removes them.

Okay --- how does this group do it? It honestly seems mostly straight-forward and basically altogether brilliant.

The reference for the technique is this very very long paper in Cancer Research from 2016. You won't find the technique mentioned until the 3rd from last method section (at the very end of the manuscript).  It utilizes an iodoacetamide desthiobiotin. If there isn't anything stuck to your cysteine then the iodoacetamide can get in there and bind to it and you can pull it down with whatever things bind to biotin. If your cysteines are bored and doing nothing, then you should pull down lots of them. If the cysteine is hanging out with some cool molecules or another cysteine then you can't pull them down. This sets the stage for the paper I started talking about at the very beginning of all these words.

Vinogradova et al., takes this technique way out further. They add in multiplexing with TMT. They add in multiple types of T-cell activation and what appears to be a bunch of different T-cell affecting compounds. I know just enough immunology to be vaguely aware that T-cells are involved in immunology somehow. All these beautiful charts of seemingly profound immunological findings make me glad that there are immunologists out there!

I'm a little confused on the labeling techniques. The supplemental makes it look like the desthiobiotin iodoacetamide is added and then normal reduction and alkylation are performed afterward, which makes it seem like you'd then remove the fancy iodoacetamide and put the normal one on, but I'm sure this is a weakness in my understanding of the technique steps.

The TMT quan all goes MS3 style on an Orbitrap Fusion, following high pH fractionation, resulting in a tremendous number of IDs. When they say in the title they're trying to make a gosh darned map of T-cell activation stuff, they aren't joking around. Once cell type and 21 fractions ran out on what is approximately a 3 hour gradient?!?? That'll get you some coverage. Minor note, they use the Waters BEH C-18 column which I've heard really good things about recently and I wasn't aware came in a sub-2 micron particle size (25 cm column).

The data is processed using IP2 AND ProLuCiD

What do we get for all the work this team did? Probably the most in-depth understanding of the cysteines in T-cells that can be affected by small molecules that we've ever had. It doesn't take much imagination (or...obviously...understanding of immunology...) to see where this could be powerful in the direct design and monitoring of drugs to activate or repress specific functions of immune processes!

Big shoutout to Dr. Matt Labenski for tipping me off to this great study!

Dramatic remodeling of 700 proteins in the surfaceome when messing with 6 oncogenes!!

I'm in complete awe of what an absurd amount of work this new preprint represents, but even more than that -- wow -- what a shining example of how we need to move to the surfacome to figure how how cells are responding stuff.....

So much work here...where to start....

Okay -- so they used Dr. Josip Blonder's favorite cell line in the world (and I'm pumped to see his work recognized in a study this great) -- there is some reason that MCF10a is important and I know he told me 100 times. I think that it might not have any of these cancer genes activated or something. And that makes sense in the context of this study.

This great multi-institution team messed with 6 different oncogenes in this cell line
AND
they messed the cells up with drugs that inhibit some of the downstream processes of these oncogenes. See the matrix expanding?

SILAC is employed here in some experiments, and maybe I'll clarify which ones in my head as I'm going through it.

Proteomes were directly harvested the way we'd normally do -- just from 20x10e6 cells, in troplicate....which, don't quote me, I'm pretty sure qualifies as more or less a fuckload. The digests were then enriched for glycopeptides using a lectin kit.

In case the lectin kit didn't pull down enough stuff they also did SAX chromatography to enrich for glycopeptides as well.

Okay -- so now they've got a more or less normal glycoproteomics setup for the cells, albeit on a pretty large scale. 

There are two ways to go after glycopeptides -- enrich pre-digest or post-digest, as I just described. they also do the other one -- cause -- you know, why not?  This is the method Josip uses, I'm pretty sure, where you oxidize the sugars and then use a biotin thingy to pull out all the proteins --- AHA!! this is the part where the SILAC is employed and this is what they consider the surfaceome. The glycoproteome is more of for filling in the background. The SILAC is for gold standard quan of the surfaceome changes.

The SILAC surfaceome is studied with a Q Exactive Plus. The glycoproteome and...well...proteome...you've got to do that while you're here, right? Goes on the super fast Lumos system using the restricted mass range method some people from this group demonstrated just a few weeks ago.  The Lumos also has activated ETD, cause...well....of course it does....

Results?

This is such a cool paper to read through. Take this statement "Proliferative oncogenes cause large changes in the surfaceome that are diverse in detail but have common functional themes."

That's the title of figure 2 and it's just marvelous. There are patterns to carcinogenesis (that's a word, right?) Yes, there are a couple genes that if they're messed up, you're screwed, but there are so many checks and back up systems that cancer is a disease of lots of things going awry most of the time.

I might have rambled about this recently, but it's always going down through the MAP Kinase things. Or at least it always seems to. How do you interpret that? I don't think you do. I think you go to the surfaceome and see how hundreds of proteins are changing in dozens of different GLYCOSYLATION THINGS and that's the fine tuned responses. All that stuff downstairs in the cytoplasm? That's so far from the action that it seems a little silly to be doing that ERK western blot.

If you can get very far in this one without being inspired that (glyco?)proteomics has finally arrived and it's time to get out of your chair and go use it to fix medicine, you're crazier than I am.

...and whoever made those figures deserves an honorary art degree or something....and should put a tutorial online about how to keep a certain preprint server from rendering your images in 41 dpi....

Need more power out of that protein quan output? Reinterpret with MSQRob!

Have you got a beautiful output out of your favorite software with thousands of quantified proteins but you're still at this point?

Do I ever have amazing news for you! What if you could just take that quantitative output (for real -- your MaxQuant or mzTab output CSVs) and reinterpret that quantification side of it with a super easy, shockingly powerful. inference tool? Maybe that's what you need to push you toward that biological interpretation!

Introducing MSQRob!

BOO? You already knew about this? Cause it's been out for 2 years?

Okay -- well, I didn't and I literally love it.

Edit: You can download it here.

Also Edit 6 months later for why you should use MSqRob and it's this literal quote: " an innovative solution to this key issue by introducing a hurdle model, which is a mixture between a binomial peptide count and a peptide intensity-based model component. It enables dramatically enhanced quantification of proteins with many missing values without having to resort to harmful assumptions for missingness"

First off -- if you're all fancy and smart and stuff, there is an R package for it and it's got an amazing walkthrough. HOWEVER --  it has a Visual Basic GUI thing that launches a Shiny App so you don't have to do anything at all.  It installs it right on your PC.

...which...well......I mean....since I've got a choice, I'm 100% certain I'm doing it this way.

Why is it better? Well, I've got 2 or 3 papers open right now and I'm confused which one is which, but it has something to do with a razorback gorilla ridge reduction or something. Its definitely some fancy stats stuff that I wouldn't be able to do otherwise.

In one of the papers they go to a ground truth dataset (the E.coli/HeLa spike in Fusion files from Qu lab at Buffalo) and it rocks the comparisons versus everything else they compare it to.

My favorite part is that this is a bonus. I'm using it as an additional interpretation on top of a dataset that I've already processed. It doesn't cost me anything but the time to label my columns appropriately.

Also -- it's compatible with everything. Lazy people like me can run it in Windows with the cool Shiny thing. Or you can use Linux AND it can be scaled up on a cluster.

And I stumbled into a goldmine of Rob Lowe/Chris Traeger gifs and images putting this together, and it's been literally impossible to pick my favorites, but this is pretty great....

Integration techniques for "multi-omics" data! -- INTEGROMICS!

I am ALWAYs up to add another "-omics" as long as you don't mean you did a western blot or set up an SRM experiment for 4 small molecules and are going to call it "-omics". I need a hard cutoff where I do/don't make fun of the suffix.....

This amazing open resource deserves far more time than I have to give it this morning, but you should check it out -- this is Vogel lab's review of answers to the question "WHAT DO YOU DO NOW THAT YOU'VE ACQUIRED MULTI-OMICS DATA!?!?!?!?!?"

We got tired of the word "next-gen" and we've started using "new age"....with varying degrees of acceptance from the community....

Are your collaborators growing cells? You need the cRFP!

This new resource presented in JPR is a simple and great idea!

It also might explain a lot of things you might have seen using big databases for proteomics. When Amol's team at OptysTech started building their cloud based search engine, they kept coming up with tons and tons of cow proteins. Like -- way more cow proteins than what you'd expect from the fact that humans and cows are mammals. Every file we sent them was from cells grown in media. And people grow these cells in media supplemented with FBS. This does not stand for Flower and Balloon Supercharger. It is a super complex media derived from COWS.

This group goes through and characterizes the stuff that they find it in and show that subtracting it out (the same way you're probably using cRAP) and then all the sudden doing secretome (looking at the proteins released into the media) makes sense!

We should probably get into the scary thought of the reproducibility of FBS sources, vendors, lot to lot variability, time of FBS on the shelf, etc., somewhere else. I know one of the companies making the burgers that I can't personally distinguish from dead cow burgers has been posting openings for proteomics people, so maybe we can have an Impossible FBS soon? I mean, come on, vendors can't seem to make the same monoclonal antibody reliably, you can't tell me that every container of something this complex is the same....I mean...I hope it is, but it seems pretty unlikely.

At the very least make a new FASTA for proteins to ignore when you're running proteins from mammalian cell culture!

Sequence-mask-search-hybrid thing finds more HLA peptide IDs!

I don't get the maths in this new study, but I do like getting more peptide identifications.

What is a sequence-mask-search hybrid de novo peptide sequencing framework? No idea. I assumed the 3 hyphenated words were a stats or math thing I've never heard of, but if it is, Google also hasn't heard of it.

I looked at all the Greek letters and decided to just download the supplemental material. They appear to train this Mask Search thing on some proteomics data and then they go after some HLA peptide data and find more peptides than anything else they search it with.

Exciting! Maybe this is the search engine and FDR for +1 peptides?!? At long last!

Unfortunately, while this group reports more identifications, the data they use isn't what I was hoping for. Out of 157,000 MS/MS scans searched, only 3,023 +1 ions were fragmented. This is a cool dataset, if all the peptides were +2 charged, peptidomics is WAY less scary!

What about the K/R found in this set?
Out of around 92,000 IDs

44,700 lysines
32,700 arginines

The average charge state of +2 checks out.

I'm not going to hit the big orange Publish button unless I can find something cooler in this paper -- back to the nerdy maths -- how did they support that all this work they did was worth publishing?

...okay...that's not too shabby, right? They went back to mass accuracy shifts at multiple positions as  a metric of match quality....I can get behind that enough to hit the button.

It's all Python stuff that can be downloaded here.

Human Proteome Project Guidelines version 3.0!

For those of us on the edge of our seats for this -- the new HPP Data Interpretation guidelines are finally available. The big highlight is probably how to incorporate Data INdependent Acquisition (DIA) data into our biggest effort to map the human proteome.



How many proteins are we up to that have significant evidence (called PE1 proteins)?

17,694 -- meaning there are some 2,000 proteins that human may or may not produce (crummy evidence PE2, PE3 or PE4, based on how crummy the evidence is -- these are the "missing proteins" that sometimes pop up in article titles).

On top of this -- I think this is probably the best part of the new guidelines -- and I'm going to steal the text and draw a red line through the link that doesn't appear to work --

This doesn't sound very efficient from an evolutionary standpoint but we have some regions of our DNA that will lead to the production of completely identical proteins. As they mention here it probably makes sense from a regulation perspective (whatever promoter thingies they are under control of) or there has never been sufficient selective pressure against having identical copies of the same proteins produced by different genetic regions. Either way, it could be problematic to assign them to a single protein group based on linear sequence homology alone, so they should be categorized as individual proteins despite the homology.

Does this make a change in most of our day-to-day work, even if we're doing human proteomics all day? Probably not, but it is interesting to think about and if you run into it on neXtpRot or the hUmAN pROtein aTLas, now you know why.

A 30 year journey of electrospray ionization!

Electrospray ionization isn't perfect, but it sure has been an amazingly flexible solution for an absurd number of things!

This short comment at Nature Communications is a great history and perspective of the 30 years since it was first shown to ionize without destroying everything!

Scheduling PRMs for lots of metabolites and/or phosphopeptides!

I've spent a lot of time with this great paper this week, and even though I posted it on another blog a long time ago I just realized yesterday how useful it is for scheduling PRMs for proteomics.

Here is the scenario -- you discover all this cool stuff with your super cool ultra high resolution system -- now you need to verify that this doesn't just occur in your 20 patients from your concatenated highly fractionated pool -- this repeats in your 300 person cohort. If you've got a super fast triple quadrupole system (or "double quad" [if you're using a Perkin Elmer [please DON'T use a their triple until they become 100% Skyline compatible! Don't let my mistake(s) be your mistakes!]) maybe you don't care. If you are pulling 600 scans a second, just copy your whole list over to it and don't schedule a thing. Set up a 10 minute run and walk away.

However -- the fastest Orbitrap is going to knock out 40 scans/second. That's a lot less targets and if you're targeting you probably want to allow a decent amount of fill time -- the Orbi still can't get as much signal in 10ms as a triple quad can with 10ms of dwell time.

In this study the authors demonstrate an easy way of generally timing PRMs and it works better than you'd believe.

Metabolomics length runs (sub 30 minutes)
PRM targeting on a D30 (Q Exactive Classic, so maximum 13 scans/second!)
237 metabolites quantified!
The scheduling is worked out more along the lines of segments rather than ultra tight scheduling. I think I told someone this week they did "early" "middle" and "late" eluters. A quick reread suggests that they broke these into closer to 7 segments based on m/z.

I think this provides a great idea of the upper limit of what is possible when targeting with PRM on an Orbitrap instrument.

While on this topic, I would like to mention another resource that should be getting more attention for this sort of thing -- focused on phosphorylation site PRMs!

At the Phosphopedia you just find or build your pathway and it builds your PRM assays. If something exists for other targets and I don't know about please email me!

Here I just chose the AKT phospho pathway -- the whole thing -- and it makes a list of my PRMs and I give it the +/- retention time wiggle that I think my nLC system has (+/- 5 min on a 60 min gradient)

-- and here is my targeted list (import directly into your QE as is) but here is also the schedule density. Around 10 minutes I'm looking at 20 targets. If I'm using a QE Classic and matching my fill time to my transient (roughly 50ms fill time with about 14ms of overhead) 64ms x 20 = I'm only going to get a PRM on each individual target ever 1.2 seconds. Just fine if I've got a 30 second peak width --I could probably get away with adding a few more targets at the 10 minute time point -- not fine if I'm doing UHPLC and my peak width is 4 seconds. I'll either have to work on narrowing that retention time, or drop some targets out of this run.

A proximity biotinylation map of human cells!

This image of how BioID works was brazenly stolen from Creative Biolabs -- sorry, please let me know if you want me to take it down -- because they already offer this BioID service!

I'm using this because the preprint has some strict copyright statements on their pictures, however -- this is worth checking out.

What is it?

Oh -- just a measurement of how 4,000 + proteins hang out with each other. This is a hot new technology (at least, I never heard of it until recently -- okay -- I'm just dumb -- here is a 2013 paper  -- wait -- i think it has become a little more refined over time...)

How's it do it?

You use a "promiscuous" protein ligase that you FUSE to your protein of interest. Then you dump in a bunch of biotin. The end of your protein with the fusion can only cause a biotin connection with things that are in close proximity to it. Apparently people have pulled this off in vivo?

I guess there are some assumptions -- like your fusion doesn't completely negate your protein - protein interactions? Anyway, if it is close then -- BOOM! the biotin gets all ligated and then you enrich your proximity proteins with normal biotin enriching thingies.

Does this mean that this group fused a few thousand proteins? Yeah -- I think it does....?

Does that sound crazy? Yes...it totally does.

Work was done on an Orbitrap Elite using a high/low (Orbitrap CID-Iontrap method). My assumption is that they bought it right when it came out for this project (ASMS 2012?) and this paper is the summary of what that Orbitrap was doing the last 7 years or so....

All the data is deposited at PRIDE, but I can't find it on this tablet since there are 20 papers in 2019 on this BioID stuff that have deposited data and I'm sleepy. (A lot of times the PRIDE doesn't go live until the paper is accepted anyway)

PfEMP1 variants identified in children with severe malaria!

I don't have time to spend on this but I can't wait to read it! 

You can't build drugs to target malaria in large part because of the presence of "var" genes. The "var" stands for variable and it's exactly what you'd think. Messed up, mutated, switched around and hard to target because of this.

This new paper provides a pipeline that integrates sequencing of the parasite with proteomics to the variants expressed, potentially opening the door to focused and targeted therapies. Maybe that's a stretch (if you trust me on the biology stuff, you don't know me very well) but -- at the very least it is a pipeline in the right direction and it works in real infected human samples!

Bioinformazing -- YPIC 2018 Results

The results of (Young Proteomics Investigators Challenge) YPIC 2018 are out and -- this is just amazing all around. I rambled about last year's a little here. 

2018 was a step up in complexity from all sides --

 How crazy is that?  One -- that this challenge is possible to set up at all!?!  And 2 -- that some scary talented young investigators solved it?

You can find out more about YPIC and join it here.