What genes can't humans live without?

This stuff has all been done on bacteria long ago, but these guys did this study in human cells. What genes (proteins!) are 100% essential for human cells to survive? Probably worth clarifying this statement -- what genes are 100% essential for human cells to survive in culture...cause thats the only way you're gonna pull this one off.

They come up with a list of 2,000. Wow, right? Depending on how you look at that, its either really big...or surprisingly small. I can't decide.

Are we surprised at all that a ton of them are proteins involved in protein glycosylation?  Apparently that stuff is important.

Link here!

Turkey proteomics!

In celebration of my country's love of giant slow-cooked birds, I present you with this year's top hit on Google for "Turkey Proteomics!"

A EUPA meeting the third week of June in Istanbul?!?!?  I LOVE the name of the meeting:

...Standardization and Interpretation of Proteomics.

WGCNA -- Another way to post-process your quantitative data

I think its about time we start some concerted spy missions. The genomics people have all sorts of cool tools. Lets send proteomics people to genomics meetings and then just steal all their cool ideas, which is probably less like stealing because they give away virtually all of their algorithms.

Case in point? This WGCNA thing. Notice that its been around since 2008. So..around the time we were all getting our heads wrapped around target decoy searches, the genomics people were like "hey, lets do some unsupervised clustering of our thousands of quantitative changes and see what stands out in a hierarchical sense"

What's it do? It tries to find patterns in your complex data without you spending all day looking up genes that make sense. Its just pulling out common traits and clusters them with your sample(s) and type(s).

How's it do it? Well, its in R, so it does it in the ugliest way possible. Does it look like the screen of a Commodore 64? Yup! Then its likely R, he world's most powerful and utilized statistical software package!

Besides that? I have no idea. Google Scholar informs me that the initial paper describing this algorithm has been cited nearly 1,000 times. So, somebody has liked this paper. Maybe I'd even toy with it before trying to check the Stats.

You can visit the WGCNA website here. It has links to papers and full tutorials.

Shoutout to Alexis for mentioning this algorithm to me a second time and showing me cool clustering data from it so I'd remember to share with people!

Ready to get HYPE(d) and AMP(ed)?

At first you might think I was initially a fan of this paper because looking up the protein and nucleotide in the title is a gold mine on Google images. I'm not saying you're wrong, but there is more to this cool paper even than the JPGs I'm probably going to insert while I'm writing this.

A few years ago people were pretty hyped about AMP. Remember this?

It looked like AMP was going to be a great big important PTM.  Turns out, however, that its an absolute pain in the foot(?) to study with LC-MS. A couple labs gave it a good hard try and got some nice results, but their techniques maybe looked a little too painful for us to want to replicate.

In this new study in MCP from Malgorzata Broncel et al., we get to see a high throughput, high sensitivity, high resolution way of studying this modification!

How'd they do it? Without radiation!  They used inert chemical probes that are attached to analogues of AMP. This technology was developed by other groups for doing visualization or protein arrays. Turns out, though, that they make a nice target for selectively pulling down proteins that had picked up the labeled AMP, and are conducive to ionizing and fragmenting in a Q Exactive.

If you are interested in this PTM, you've got a protocol now!  And along the way they studied some super important protein named HYPE that has a lot to do with bacterial infections and may lead to nice drug targets for us to use in the post-antibiotic era!

Single protein equilibrium in a cell

Wow. This is just fascinating!  Thanks PastelBio for sharing.

This link will take you to what I'm talking about. Its a press release of sorts about a new study in Nature Physics. Interestingly, from the time of the press release until now, it appears that they decided to change the name of the paper and it is available here (paywalled, sorry!)

The press release explains it better than I can, honestly, but they took that crazy super computer down in Tennessee, the Titan (19,000 CPUs AND 19,000 GPUs)

What they did was single cell protein modeling with all that processing power.

What did they conclude? That considering protein production speed and turnover via degradation, proteins may never actually achieve equilibrium within a cell. That you'd constantly be looking at a moving and changing population of even one single gene product. So if you were able to pull out just two proteoforms of a given gene product at any point in time, they probably wouldn't be the same.  One might be just formed and the other might be partially degraded...or reacted, or modified.

Is it real? Who knows? Its fascinating, though!  I guess it probably doesn't affect what we do much. I mean, we're looking at the averages of signals from thousands of copies of proteins from thousands or millions of cells at once, so maybe all of this averages out into just noise, but I always love things that highlight how little we still know about biology.

Can you run Proteome Discoverer on Windows 10?

I've been asked this question a couple of times. And maybe now I know the answer.

At first run, it certainly looks like PD 2.1 installs just fine with nothing special whatsoever on Windows 10.

Now...that being said, this isn't officially supported by the vendor. And just because the couple HeLa runs I just did seem to go just fine doesn't mean that every feature will be ready...but, again, I haven't had a problem yet!

Nice and short review on proteogenomics in Springer

Like an awful lot of people right now, I'm kind of obsessed with the magical thing called "proteogenomics". Which...honestly...seems a little bit like magic. Getting good quality transcriptional data and filtering it so that you can see new mutations....that you can trust...AND THEN using this information to find new matches to your MS/MS data....that you can trust....

A few people have totally pulled it off....and I have the papers stacked up in front of my PC all marked up and highlighted and...well...maybe I'm dumb...but I still don't know how they did it.

For a good starting point, check out this nice review from Sam Faulkner et al.,. While I'm on the topic of magic, you might be surprised to see this is an article from Springer that isn't behind a paywall!

Oh yeah! And here is the link!