Recently read - Jan 2010

Blueprint for antimicrobial hit discovery targeting metabolic networks
Y. Shen, J. Liu, G. Estiu, B. Isin, Y-Y. Ahn, D-S. Lee, A-L. Barabási, V. Kapatral, O. Wiest, and Z. N. Oltvai

The authors use a flux balance analysis to identify reactions that essential for S. aureus growth. The enzymes required for the identified pathways were selected for in silico drug screening using both known structures and homology models. Inhibitors identified computationally where tend tested experimentally. I particularly liked the breath of different methods used in this study (FBA, homology modelling, ligand docking and experimental verification). It shows the usefulness of the increase in knowledge across these different areas (networks and structures).

Quantitative Phosphoproteomics Reveals Widespread Full Phosphorylation Site Occupancy During Mitosis
Jesper V. Olsen, Michiel Vermeulen, Anna Santamaria, Chanchal Kumar, Martin L. Miller, Lars J. Jensen, Florian Gnad, Jürgen Cox, Thomas S. Jensen, Erich A. Nigg, Søren Brunak, and Matthias Mann

In this study HeLa cells were synchronized in different stages of the cell cycle and their proteins and phosphorylation sites were quantified relative to asynchronous cells using a SILAC mass-spec approach. Changes in protein abundance and phosphorylation were combined with transcriptional changes and these were used to identify previously known and potentially novel complexes and kinase-substrate interactions important for cell-cycle progression. In addition, I thought it was pretty cool that the authors found a way to directly quantify the phosphorylation site occupancy from the mass-spec results. I was only slightly disappointed that the authors did not attempt to do a cross-species analysis given the available data from Liam Holt et al. on Cdk1 phosphorylation in S. cerevisiae. (Bonus- spot the blogger in the author list)


The Genetic Landscape of a Cell
Michael Costanzo et al.

This paper reports the large scale effort to quantify genetic interactions for approximately 1700 times 3800 pairs of genes in S. cerevisiae. As it is typically the case for these sort of "resource" papers describing a large dataset there is no way that a paper can do full justice to this work. They have mostly tried to show different ways to use this information: 1) predict gene function, 2) functional interactions between complexes and functional groups and 3) prediction of drug targets. Hopefully cell biology labs will pick up on this information to search for their genes of interested and bioinformatics groups will continue to find ways to make these resources easier to navigate (see STRING for a good example of this).

The joys of print

For the past two months I have been enjoying my first ever print subscription to a scientific journal. The good folks over at Nature Reviews Genetics offered my a small discount that nudged me to it (thank you!). I thought that if I ever tried a print subscription it would be for sure a review journal. I can't say I regret the decision. Having the print issue to read on my commute makes me read articles that I would not normally print out and the front section (research highlights) is a good way to catch up to science news.

 

Maybe this explosion of e-readers will make it easier to emulate the browsing experience of a bound print copy. As Nicholas Carr (and others) have pointed out, changes in internet technology shape the way we think and use information. In a recent post, Carr gives his answer to this year's Edge annual question "How is the Internet changing the way you think?".
He writes:

"My own reading and thinking habits have shifted dramatically since I first logged onto the Web fifteen or so years ago. I now do the bulk of my reading and researching online. And my brain has changed as a result. Even as I’ve become more adept at navigating the rapids of the Net, I have experienced a steady decay in my ability to sustain my attention."

I think that Carr is, as usual, exaggerating in his pessimist view of web culture. In my work I scan online and print to read. The difference from that workflow to browsing a bound print copy is only the way I filter what I read. Still, it is worth a thought. Maybe we should be working on technologies that will help us work by forcing us to focus our attention better. For now, a print out will do :).

In science, data without purpose is sometimes required

The title is probably flamebait but it might get you to read my little rant about data production in science. Its something I have been meaning to write about for a while but Deepak's post provided the extra incentive.

I think Deepak's post was a reminder that science is nothing without hypothesis and I certainly agree with that. To put this into context maybe it worth pointing again to the wired article about "The End of Theory" where Chris Anderson painfully tries to make the point that with the deluge of data that we are seeing we don't need models or hypothesis we just need to crunch the data to look for correlations.

I strongly disagree with this viewpoint. What would we learn about reality this way ? At most we would see correlations and could have some predictive power about future events but we would not know the mechanisms and thats the interesting part.

So why is data without purpose sometimes justified ? What I mean by this is that the capacity to produce data and its analysis does not have to be centralized in the same place. My perspective (bioinformatician) is from someone that has benefited a lot from the data deluge in biology and the fact that data is made (mostly) available to others. It has allowed many studies that reuse pre-existing results to answer new questions.

I also work in lab the develops genetic interactions screening methods and end having some discussions about this topic. Many people dislike this sort of research, finding creative names like "fishing expedition" to describe it. The truth is that there are many types of data that we need to collect (genomes, gene expression, protein-protein interactions, etc) that we know that will be useful to understand how cells work. We just need more accurate and cheaper methods to get them and there is no other way but to have the focus of the research be the data production itself.

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.