Evolution and Function of Post-translational Modifications

A significant portion of my postodoctoral work is finally out in the last issue of Cell (link to paper). In this study we have tried to assign a function to post-translational modifications (PTMs) that are derived from mass-spectrometry (MS). This follows directly from previous work where we looked at the evolution of phosphorylation in three fungal species (paper, blog post). We (and other groups) have seen that phosphorylation sites diverge rapidly but we don't really know if this divergence of phosphosites results in meaningful functional consequences. In order to address this we need to know the function of post-translational modifications (if they have any). Since these MS studies now routinely report several thousand PTMs per analysis we have a severe bottleneck in the functional analysis of PTMs. These issues are the motivations for this last work. We collected previously published PTMs (close to 200.000) and obtained some novel ubiquitylation sites for S. cerevisiae (in collaboration with Judit Villen's lab).  We revisited the evolutionary analysis and we set up a couple of methods to prioritize those modifications that we think are more likely to be functionally important.

As an example, we have tried to assign function to PTMs by annotation those that likely occur at interface residues. One approach that turned out to be useful was to look for conservation of the modification sites within PFAM domain families. For example, in the figure above and under "Regulation of domain activity", I am depicting a kinase domain. Over 50% of the phosphorylation sites that we find in the kinase domain family occur in the well known activation loop (arrow), suggestion that this is an important regulatory region. We already know that the activation loop is an important regulatory region but we think that this conservation approach will be useful to study the regulation of many other domains. In the article we give other examples and an experimental validation using the HSP70 domain family (in collaboration with the Frydman lab).

I won't describe in detail the work as you can (hopefully) read the paper. Leave a comment or send me an email if you can't and/or if you have any questions regarding the paper or analysis. I also put up the predictions in a database (PTMfunc) for those who want to look at specific proteins. It is still very alpha, I apologize for the bugs and I will try to improve it as quickly as possible. If you want access to the underlying data just ask and I'll send the files. I am also very keen on collaborations with anyone collecting MS data or interested in the post-translational regulation of specific proteins, complexes or domain families.

Blogging and open science
Having a blog means I can give you also some of the thoughts that don't fit in a paper or press release. You can stop reading if you came for the sciency bits. One of the cool things I realized was that I have discussed in this blog three papers in the same research line, that run through my PhD and postdoc. It is fun to be able to go back not just to the papers but to the way I was thinking about these ideas at the time. Unfortunately, although I try to use this blog to promote open science this project was yet-another-failed open science project. Failed in the sense that it started with a blog post and a lot of ambition but never gained any momentum as an online collaboration. Eventually I stopped trying to push it online and as experimental collaborators joined the project I gave up on the open science side of it. I guess I will keep trying whenever if makes sense. This post closes project 1 (P1) but if you are interested in online collaborations have a look at project 2 (P2).

Publishable units and postdoc blues
This work took most of my attention during the past two years and it is probably the longest project I have worked on. Two years is not particularly long but it has certainly made me think about what is an acceptable publishable unit. As I described in the last blog post, this concept is very hard to define. While we probably all agree that a factoid in a tweet is not something I should put on my CV we allow and even cheer for publishing outlets that accept very incremental papers. The work I described above could have easily been sliced into smaller chunks but would it have the same value ? We would have put out the main ideas much faster but it could have been impossible to convince someone to test them. I feel that the combination of the different analysis and experiments has more value as a single story but an incremental approach would have been more transparent. Maybe the ideal situation would be to have the increments online in blogs, wikis and repositories and collect them in stories for publication. Maybe, just maybe, these thoughts are the consequence of postdoc blues. As I was trying to finish and publish this project I was also jumping through the academic track hoops but I will leave that for a separate post.

Stitching different web tools to organize a project

A little over a year ago I mentioned a project I was working on about prediction and evolution of E3 ligase targets (aka P1). As I said back then, I am free to risk as much as I want in sharing ongoing results and Nir London just asked me how the project is going via the comments of that blog post so I decided to give a bit of an update.

Essentially, the project quickly deviated from course since I realized that predicting E3 specificity and experimentally determining ubiquitylation sites in fungal species (without having to resort to strain manipulation) were not going to be an easy tasks.
So, since the goal was to use these data to study the co-evolution of phosphorylation switches (phosphorylation regulating ubiquitylation) it makes little sense to restrain the analysis specifically to one form of post-translational modification (PTM).  After a failed attempt to purify ubiquitylated substrates the goal has been to come up with ways to predict the functional consequences of phosphorylation. We will still need to take ubiquitylation into account but that will be a part of the whole picture.

With this goal in mind we have been collecting for multiple species data on phosphorylation as well as other forms of PTMs from databases and the literature and we have been trying to come up with ways to predict the function of these phosphorylation events. These predictions can be broken down mostly intro tree types:
- phosphorylation regulating domain activity
- phosphorylation regulating domain-domain interactions (globular domain interfaces)
- phosphorylation regulating linear motif interactions (phosphorylation switches in disordered regions)

We have set up a notebook where we will be putting some of the results and ways to access the datasets. Any new experimental data and results from the analysis will be posted with a significant delay both to give us some protection against scooping and also to try to guarantee that we don't push out things that are obviously wrong. This brings us to a disclaimer... all data and analysis in that notebook is to be considered preliminary and not peer reviewed, it probably contains mistakes and can change quickly.

I am currently colaborating with Raik Gruenberg on this project and we are open to collaborators that bring new skills to the project. We are particularly interested in experimentalist working in cell biology and cell signalling that could be interested in testing some of the predictions we are getting out of this study.

I won't talk much (yet) about the results we have so far but instead mention some of the tools we are using or planning to use:
- The notebook of the project hosted in openwetware
- The datasets/files are shared via Dropbox
- If need arises code will be shared via Google Code (currently empty)
- Literature will be shared via a Zotero group library
- The papers and other items can be discussed in a Friendfeed group

This will be all for now. I think we are getting interesting results from this analysis on the evolution of the functional consequences of phosphorylation events but we will update the notebook when we are a bit more confident that we ruled out most of the potential artifacts. I think the hardest part about exposing ongoing projects is having to explain to potential collaborators that we intend to do so. This still scares people away.

I'll end with a pretty picture. This is an image of an homology model for the Tup1 -Hhf1 interaction. Highlighted are two residues that are predicted by the model to be in the interface and are phosphorylated in two different fungal species. This exemplifies how the functional consequence of a phosphorylation event can be conserved although the individual phosphorylation sites (apparently) are not. 

Reply: On the evolution of protein length and phosphorylation sites

Lars just pointed out in a blog post that the average protein length of a group of proteins is a strong predictor of average number of phosphorylation sites. Although this is intuitive this is something that I honestly had not fully considered. As Lars mentions this has potential implications for some of the calculations in our recently published study on the evolution of phosphorylation in yeast species.

One potential concern relates to figure 1a. We found that, although protein phosphorylation appears to diverge quickly, there is a high conservation of the relative number of phosphosites per protein for different GO groups. Lars suggests that, at least in part, this could be due to relative differences in average protein size for these different groups that in turn is highly conserved across species.

To test this hypothesis more directly I tried to correct for differences in the average protein size of different functional groups by calculating the average number of phosphorylation sites per amino-acid, instead of psites per protein. These values were then corrected for the average number of phosphorylation sites per AA in the proteome.

As before, there is still a high cross-species correlation for the average number of psites per amino-acid for different GO groups. The correlations are only somewhat smaller than before. The individual correlation coefficients among the three species changed from: S. cerevisiae versus C. albicans – R~0.90 to 0.80; S. cerevisiae versus S. pombe – R~0.91 to 0.84; S. pombe versus C. albicans – R~0.88 to 0.83. It would seem that differences in protein length explains only a small part of the observed correlations. Results in figure 1b are also not qualitative affected by this normalization suggesting that observed differences are not due to potential changes in the average size of proteins. In fact the number of amino acids per GO group is almost perfectly correlated across species.

Another potential concern relates to the sequence based prediction of phosphorylation. As explained in the methods, one of the two approaches used to predict if a protein was phosphorylated was the sum over multiple phosphorylation site predictors for the same sequence. Given the correlation shown by Lars, could it be that, at least for one of the methods, we are mostly predicting the average protein size ? To test this I normalized the phosphorylation prediction for each S. cerevisiae protein by their length. I re-tested the predictive power of this normalized value using ROC curves and the known phosphoproteins of S. cerevisiae as postives. The AROC values changed from 0.73 to 0.68. This shows that the phosphorylation propensity is not just predicting protein size although, as expected from Lars' blog post, size alone is actually a decent predictor for phosphorylation (AROC=0.66). The normalized phosphorylation propensity does not correlate with the protein size (CC~0.05) suggesting that there might ways to improve the predictors we used.

Nature or method bias ?
Are larger proteins more likely to be phosphorylated in a cell or are they more likely to be detected in a mass-spec experiment ? It is likely that what we are observing is a combination of both effects but it would be nice to know how much of this observed correlation is due to potential MS bias. I am open to suggestions for potential tests.
This is also important for what I am planning to work on next. A while ago I had noticed that prediction of phosphorylation propensity could also predict ubiquitination and vice-versa. It is possible that they are mostly related by protein size. I will try to look at this in future posts.

Open Science - just do it

My blog is 5 years old today and to celebrate I am trying to actually do some blogging. There are a couple of reasons why I have blogged less in the past months. In part it was due to FriendFeed and also in part because I was trying to finish a project on the evolution of phospho-regulation in yeast species. Nearing the end of a project should actually provide some of the most interesting blogging material but I did not ask for permission from everyone involved to write about ongoing work.

I have to admit that although I have been discussing and evangelizing open science for over two years I have done very little of it. I have used this blog sometimes to put up small analysis or mini-reviews but never to describe ongoing projects. I have tried to start a side-project online but I over-estimated the amount of "spare cycles" I have for this. So, I have talked it over with my supervisor and I am now free to "risk" as much as I want in trying out Open Science. The first project I will be trying to work on will be on E3 target prediction and evolution.

Prediction and evolution of E3 ubiquitin ligase targets
As I have mentioned above, I have been working in the past months on the evolution of phosphorylation and kinase-substrate interactions in yeast species. I am interested in the evolution of regulatory interactions in general because I believe that they are important for the evolution of novel phenotypes. This is why I will be trying to study the evolution of E3 target interactions. In order to get there I will try first to develop some methods to predict ubiquitination and E3 targets. Since a lot of the ideas and methodology applies to other post-translational modifications and even localization signals I will in the future try to generalize the findings to other types of interactions.

Some of the questions that I will try to address:
- How accurately can we predict E3 substrates ?
- How quickly in evolution do E3-targets change ?
- Is there co-regulation by kinases and E3s on the same targets (and how these evolve) ?

Once I have something substantial I will open a code repository on Google Code.