I don't have proof yet that this is as good as I think it might be. If it is, this is a serious missing link in proteomics filled in (courtesy of free software!!!)
What I might be ridiculously excited about is the PTMOracle, which you can read about in this new JPR paper here.
If you start doing any kind of PTM study and want to move to downstream analysis you will quickly find you need to:
1) Hijack some genomics tools built years ago for microarrays
2) Use some genomics tools someone hijacked years ago for microarrays and made kinda good at proteomics (and available)
3) Do lots and lots of manual work
Those genomics tools are awesome. They really are. On the downstream analysis/data processing end, they've got a 10 year head start on us.
But -- upregulation of the entire ERK protein may do something very different in a cell than ERK phosphorylation on the normal active tyrosine (or the less common, biologically, but easier to detect with MS - active threonine site) and unmodified genomics tools are either gonna: a) ignore this 'cause it doesn't matter at the transcript level or b) lump these three effects together
Not to say this is bad (and please never trust the accuracy of biological examples I only partly remember and use for examples), but when our instruments have the capability to tell the difference between protein upregulation versus phosphorylation on one or both sites -- simplifying this down to one effect may limit or even mislead the downstream interpretation.
PTMOracle wants to bridge this gap (and, holy cow, the further I get into this paper the more exclamation points I'm adding to the title of this post, I may need to let this cool and copy edit a little.) By separately visualizing the PTMs with this tool we may be able to flesh out these differences and see the real biological patterns. The ultimate goal is to visualize, for example, this treatment shows us phosphorylation of these proteins that protein-protein interaction (PPI) studies have shown us interact -- there is a serious pattern here.
Maybe even cooler? Different PTMs! I picked the screenshot at the top over the one the authors uses for the abstract (sorry authors and JPR, if this annoys you please email me; orsburn@vt.edu I will switch it) because what if your treatment of cells leads through a pattern of acetylation? What tool on earth visualizes that for you? None I've seen. And, increasingly, these deep proteomics datasets are capable of finding even low abundance PTMs in un-enriched datasets, if you look for them! You can find that you have an acetylation or methylation signaling cascade that may lead you to the biology.)
How'd they do this sorcery?
They take huge and awesome PTM databases and they take the data from protein-protein interaction (PPI) studies and combine them somehow. There are lots of bioinformagic details, including required formatting stuff. Most of the PPI seems to derive from yeast-2-hybrid assays, but I bet you can only make this better with data from the currently on-going PPI studies (like BioPlex!)
What is important:
1) The tool is live (and downloadable right now here!)
2) The tool is free (http://apps.cytoscape.org/apps/ptmoracle)
3) They verified that the tool totally works with both publicly available yeast data and with human kinase(!!!) data.
4) If it is 10% as good as it looks in the paper, it is still worth getting excited about. If this isn't the tool that I've been dreaming of since I did my first phospho enrichment, it is at the very very least a step in the right direction!
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