COVID-19 AirBnB cancellation policies now extend through ASMS.

I don't know how many people out there do AirBnB for conference travel, but it's my go to for just about any trip. Unsurprisingly, the extension in the US now covers the dates for ASMS Houston. 

I'll undoubtedly use the travel funds for some other trip, so I actually get all my funds back. Woooo! 

Is this the long sought after list of human housekeeping proteins?!?!

A shockingly useful thing in genomics is a list of "housekeeping genes" for each organism. These things are always there and nearly always in uniform abundance for some reasons that make DNA or RNA level sense.

A list of universal "housekeeping proteins" is something I know I've always looked for -- and -- could this newly ASAP Accepted paper at JPR have that list? 

I'm paywalled right this second, so I can't say for sure, so I'm going to leave it here.

Just a reminder, though, if you DID have a list like this you can utilize those proteins in a most workflows, including Proteome Discoverer. I put together an overview of one way you can use a list like that here a couple of years ago (for normalizing between multiple TMT/iTRAQ sets).

Add super accurate and fast quantification to your workflow with FlashLFQ!

Hey!

Do you use a tool that is great for peptide ID but doesn't do quantification -- or only does spectral counting, or does quan in some other dumb way?

BOOM!

(Ignore the fancy Greek words in this title, they are not why this is important -- okay -- if you care about getting better quan, you should read about the erroneous assumptions that we often make in proteomics that makes some things seem super significant when they clearly are not)

What you should do is go get the FlashLFQ GUI here!

 You get all these smart features in a really easy way to use (flexible formats -- including Match Between Runs (MBR) and the Bolognese Nacho Impartial Hat Turkey Carburetor thing that the paper is about!

ASMS 2020 Houston Cancelled -- Online ASMS 2020 details coming soon!

In something that comes as a surprise to no one, we won't be gathering in Houston for my favorite holiday.

An online format is coming. Keep up on details at ASMS.org.

If you're crazy enough that you booked your Airbnb a year in advance, you may be able to get forgiveness for all or a percentage of your stay. May 31, 2020 is the last day currently, but details will be posted here.

Man...a lot can change in the world in 3 weeks. The Skyline team was being proactive about the volunteer speaker pool and I was convinced we'd beat this stupid virus (come on, it's only 10 proteins!) by then.

Great virus proteomics references!

This isn't anywhere near a complete list, but if you're finding yourself motivated to jump into virus proteomics all the sudden for some weird reason or the other, here are some awesome resources.

This review is where everyone (IMHO) should start.

If you're looking for something beyond what the 137 references in that paper can provide, you might need to do some jumping jacks.

However, if you wanted to focus on specifically LCMS methods for virus stuff, a really good next stop would be going to Google Scholar and typing in Virus and Arthur Moseley. That team at Duke has been getting DARPA funding to work on virus detection for years (that's who is acknowledged in at least some of the papers).

In this one they identify host targets using 2D-wait...gels...?...hey, it works and then they build targeted assays for the host biomarkers. 

And this one is my current favorite. In this one they build targeted assays for viral proteins!

The peptides are hunded down with an LC-QTOF and LC-MRM (all Waters instruments) are developed. Stable isotope peptides are used for the MRMs and 30 minute NanoLC gradients.

On the TQ-S they get theoretical LODs in the sub-femptomol range!

AND they show that they can use targets for the variable regions of the nucleocapsids to work out the lineage of the virus.

UniProt got organized on COVID-19!

UniProt just launched this great new page, linking all their COVID-19 resources. I expect a lot more with UniProt2020_02 in just a few weeks.

https://covid-19.uniprot.org

DeepLC -- Predict the retention times of MODIFIED peptides with a handy GUI!

I think my expectations are typical for what I expect from the good people of the world making free software and proteomics tools. 

I just want: 

1) Completely new ideas that are way better than the old ones. 

2) Ultra powerful algorithms that use resources I couldn't possibly get or use elsewhere.

3) It all bundled in a way that will only take me like 45 seconds to install.

4) It to be intuitive enough that I don't have to read anything to use all this power.

All the perfectly reasonable expectations we all have for our bioinformagicians out there. 

DeepLC is a new program that checks all these boxes! 

We know that chemically modifying a peptide with a PTM shifts it's retention time. How? That depends on the modification. Phosphopeptides generally come out earlier (you probably lost a significant number of them if you used a PepMap trap column.), but what about the other ones?  That's one of the problems you need DeepLC for. Loads of application here, but I've gotta move fast today.  

You can get the program from this Github!

Super easy installation and it only has 8  settings! Tons of new power for me in exchange for exactly zero effort on my part?

Proteomics + Mice on Turntables!

I'm taking 15 minutes to stop looking at a FAIMS mystery to just, honestly, not look at it for a second and -- BOOM -- someone put mice on turntables and did proteomics of their brains?

I'm also going to grab breakfast/lunch/what day is it? so I can't read this, but I'll put the link here! so I can come back later.

...and a figure from the abstract to prove I'm not joking!

...apparently it is about learning about how mice learn. I'm going to still do the joke I planned.

#JBC MethodsMadness final round Team mass spec needs your votes!

If you weren't aware this is a thing, it's down to the finals and team mass spec needs your votes.

You can vote here!

Yes....um....CRISPR and PCR were beaten in the earlier rounds...because...hmmm....

Okay, but mass spec is DEFINITELY cooler than Cryo-EM. If you don't use a UHMR or EMR to speed up the workflows, even with an inexpensive Cryo-EM ($2M) to do all of the QC work and optimization stuff for your normal Cryo-EM (maybe $8M) you can only solve 4 or 6 protein structures per year with one.

Prosit spectral libraries for COVID-19 (SARS-CoV-2) vs experimental!

Okay...so...I'm honestly just floored by how ridiculously accurate these Prosit spectra are... Everyone should be using this tool. (Click should expand it.)

First off -- This tool doesn't require you to be a master bioinformagician or anything to use it.
Here is my simple walkthrough on how I generate Prosit spectral libraries.

Second --
At the top is a mirror plot generated in Proteome Discoverer 2.4. The top is the experimental peptide from a COVID-19 / SARS-CoV-2 preprint that came out on Monday 3/23/20. The bottom is the prediction made by Prosit on 1/27/20.

 Worth noting: In earlier versions of Proteome Discoverer the MSPepSearch may not accept the Prosit spectral libraries (whateveryoucalledit.MSP). I pretty much jumped from 2.2 to 2.4 since I was doing mostly small molecule stuff for a year. If you are on an earlier version of PD 2.2, there is a solution -- MSAna (which is compatible with PD 2.1-2.4  -- all versions -- including the free versions)

You can get MSAna and the installation instructions from www.pd-nodes.org -- now you can use spectral libraries. MSAna also has several options for decoy library generation. It is worth checking out on it's own.

Back to the library vs theoretical, though!

This is the Prosit predicted fragmentation pattern for this peptide. This is a screenshot from the ridiculously handy and free tool PDV (Proteomics Data Viewer) that I use basically every day now for one reason or another.

Yo, where did y14 and y15 go? And it is kind of a neat characteristic that it only predicts that you'll see a central series of b -ions...

An experimental PSM says...?

....pretty darned close!


Okay -- that's not bad, right? However, you should go back to the top and look at the relative intensities of these fragments.....because Prosit predicts those too! Actually, here is a zoomed in clip!

A deep learning tool predicted the bottom....and the top is the real spectra from a peptide that had never been experimentally observed in unlabeled for until last Monday!! Crazy, right?

I have been pretty hard on a lot of the "artificial learning, machine intelligence, deep intelligence" stuff and I still think this is legit funny --

-- and I'm still going to be skeptical of anyone saying those terms (as we should be, of course) but I'm flipping through these spectra from this tool and this is one deep learning thingamabob that looks like its doing exactly what it's supposed to.

EDIT -- If you use spectral libraries in Proteome Discoverer -- go to the PSM level and double click anywhere on the PSM to open the normal menu you are used to. Now you'll find that this button is not greyed out. Click that and it will open your experimental vs library spectrum(a)(es)