Structure based prediction of SH2 targets

One of the last few things I worked on during the PhD is now available in PLoS Comp Bio. It is about the structure based prediction of binding of SH2 domains to phospho-peptide targets.

The SH2 domain (src homology domain 2) is a small domain of around 100 amino-acid that has a strong preference to bind peptides that have phosphorylated tyrosines. The selectivity of each domain is typically further restricted by variable surfaces near the phospho-tyrosine binding pocket. See figure below:

The binding preference of each domain can be experimentally determined using for example peptide library screening, phage display or protein arrays. Alternatively we should be able to analyze the increasing amount of structural information and predict the binding specificity of peptide binding domains.
We tried to show here that given a structure of an SH2 domain in complex with a peptide it is possible to predict the binding specificity of this domain. It is also possible, to some extent, predict how mutations on these domains might affect their binding preferences. Finally, combining predictions of specificity with known human phospho sites allows for very reasonable predictions of in vivo SH2-target interactions.

The obvious limitation here is that we need to start with structure of the domain we know from some unpublished work that for families with good structural coverage, homology models can produce specificity predictions that as accurate as from x-ray structure. The other limitation is that giving the lack of dynamics a single conformation of the interactions is modeled and this should in part help determine the binding specificity. One possible to this problem that we have used with some success is to model different peptide conformation for each binding domain.

I should make clear that although I think there is an improvement over previous works there is already a considerable amount of research on this topic that we tried to cite in the introduction and discussion. I would say that some of the best previous work on structure based predictions of domain-peptide interactions has come from Wei Wang lab (see for example McLaughlin et al. or Hou et al.)

This manuscript was the first (and only so far) I collaborated on with Google Docs. It worked well and I recommend it to anyone that needs to co-write a manuscript with other people. It saves a lot of emails and annotations on top of annotations.

Building an e-Science platform with Miscrosoft tools

(via Frank Gibson's Peanutbutter) Hugo Hiden, the technical director of the North-East Regional e-Science Centre (NEReSC) started a new blog where he will explore how to build an e-Science platform based on Microsoft technology. The initial post explains a little bit why he is doing this:
"The reason for this blog is, primarily, to document my experiences with writing a prototype e-Science research platform using Microsoft tools instead of the more traditional approach of fighting with Open Source. This way is easier, supposedly."
and also, what he aims to build:
"The task I have set myself is to recreate, at a basic level, the software being developed by the CARMEN project (http://www.carmen.org.uk). "

Let's see how it goes. Maybe they'll take suggestions later on :).

Bio-science hype cycle

I found out about the Gartner's Hype Cycle today a story at Postgenomic (via Science Notes and HealthNex)

The Gartner's Hype Cycle is meant to highlight the relative maturity of different IT technologies. The idea originated from the common pattern of evolution of human perception towards nascent technologies. From their initial trigger passing trough exaggerated expectations, disillusionment and finally to a maturity and stability.

Just for the fun of it I tried to plot the same graph with some bio-science related technologies and/or ideas:

This is a very limited and biased view but for me it was interesting trying to think were to place the different technologies.