A window on protein-carbohydrate interactions

It is a truth universally acknowledged that a single new method, especially one relevant to an area that has hitherto been resistant to study, often opens up enormous possibilities for increased understanding.  And yet, new methods often don’t get the publicity they deserve.  Possibly this is because a good new method, like a good new friend, is only really possible to identify in retrospect.  Will your new friend keep in touch when you move away, or is s/he only a good companion in a narrow geographical range?  Will the new method work in the system you’re interested in, or is it restricted to a narrow range of applications?  I don’t know how important the most recent method from Linda Hsieh-Wilson’s lab (Rogers et al. 2011.  Elucidating glycosaminoglycan—protein-protein interactions using carbohydrate microarray and computational approaches.  PNAS  PMID 21628576) is going to turn out to be; but I can tell that it’s a clever approach to a previously intractable problem.

The problem is the question of how to track interactions between proteins and various kinds of glycosaminoglycans (GAGs).  GAGs are polymers of disaccharides in which one of the sugar units carries an amine; they’re usually linked to proteins to make proteoglycans, which make up much of the extracellular matrix.  They’ve been hard to study because they’re structurally diverse — they may be made up of several different sugars, the chain can be over 100 sugar units long, and each sugar group may be modified with sulfate groups at different positions.  Hard to study does not equal uninteresting, however: GAGs regulate all kinds of biological processes, including cell growth, viral invasion, blood coagulation, and the process of recovery (or not) from that cruelest of injuries, spinal cord injury.  GAGs have been shown to help assemble multimeric protein complexes of growth factors such as fibroblast growth factor, helping the growth factor to induce signaling in target cells.  But little is known about how these interactions are controlled, because — until now, perhaps — the specificity of these interactions has been really hard to look at.

Counting phosphorylations: one, two, many…

Jeremy Gunawardena’s lab just published a paper that should probably be required reading for anyone in the habit of attempting to measure the relative levels of phosphorylated proteins using Western blots (Prabakaran et al. 2011.  Comparative analysis of Erk phosphorylation suggests a mixed strategy for measuring phospho-form distributions.  Mol. Syst. Biol. 7 482).  If you are in that category, be warned: you will find this paper depressing.

What Prabakaran et al. wanted to do was to find a way of determining the pattern of phosphorylations on a protein.  They chose the simplest situation possible — Erk, a protein with just two phosphorylated sites — and set out to develop a reliable method for finding out how much of the protein was phosphorylated at only site 1, how much at only site 2, and how much on both sites.

Did you realize that with all our technology, we still can’t do this?  Many people don’t. Quantitative mass spectroscopy techniques have recently made it possible to get a number for how much of the protein is phosphorylated at site 1 or site 2, but that still doesn’t tell you the distribution of the phosphoforms.  Suppose you have a protein that looks like this:

XXXS1XXX[cleavage site]XXXS2XXX

where S1 and S2 are the sites of phosphorylation. The [cleavage site], obviously, is the point at which the enzyme you’re using to chop the protein into peptides to run it on the mass spec acts.  When you analyze your peptides, you will have no idea whether the XXX[P]S1XXX peptides you see come from a protein in which just S1 is phosphorylated, or a protein in which both S1 and S2 are phosphorylated.  So, if you see 50% [P]S1 and 50% [P]S2, you won’t know whether this reflects a situation in which both sites are phosphorylated independently (leading to a mixed population of proteins with only S1, only S2, and both sites phosphorylated) or a situation in which S2 is only phosphorylated after S1 (50% of the protein is phosphorylated on both sites, and 50% not at all).  This could easily be biologically important, don’t you think?

Synthetic probes of natural oscillations

One of the motivations for systems biology is the gathering realization that biological systems are not simply composed of on/off switches.  Instead of thinking of signal transduction as a simple relay race — A passes the information to B, who passes it to C — we need to understand the information processing in multiple layers of feedback and feed-forward loops.  The dynamics of the components of the pathway are our best window onto the behaviors of these loops.  But the complexity of natural systems is such that interpreting protein dynamics is often not easy.  The Lahav lab has been chewing away at one such problem for a while: the dynamics of the transcription factor p53, one of the body’s most important defenses against cancer. After DNA damage, the p53 network is responsible for making the decision of whether the cell should arrest the cell cycle and attempt to repair the damage, carry on, or die.  Most cancer cells have lost the ability to choose correctly when faced with this situation.

Friday Feature: Ceci n’est pas un film

It’s Friday — but this is not a movie.  One must be fair to the non-imagers among us (theorists, biochemists and such).  You are looking at a representation of the geometry of steady-state phosphoform distribution in a system consisting of two enzymes (a kinase and a phosphatase) and a substrate with two distinct phosphorylation sites, from the work described in Manrai, AK and Gunawardena, J.  2008. The geometry of multisite phosphorylation, Biophys. J. 95 5533-5543. PMC2599844.

Why would you want to know about this?  Post-translational regulation is a hugely important mechanism for changing the behavior or localization of proteins, and phosphorylation is possibly the most important form of post-translational regulation in eukaryotes. The number of phosphorylation sites on some of the proteins we study is staggering: the EGF receptor has 10, p53 has 16, and tau (the microtubule-associated protein in the fibrillary tangles in Alzheimer’s disease) has over 40.  Since a protein with n phosphorylation sites has 2(n) possible ways of being phosphorylated, the presence of different phosphoforms adds enormously to the complexity of the mixtures of proteins found inside a cell.

How does a biological system interpret this complexity?  p53 has an impressive variety of biological functions, but it’s hard to believe that the 2(16) different phosphoforms of p53 (>65,000) each have specific biological activities.  It seems much more likely that cells use some kind of readout of the overall distribution of phosphorylations — possibly it’s the concentration of proteins with phosphorylations above a certain level that matters, or maybe it’s something more complicated than that.  It’s hard to know without the tools to analyze phosphoform distributions.

Enter Manrai and Gunawardena.  I will tread very lightly over the ground they cover: I can’t reproduce the mathematical reasoning in this format (especially since I’ve just discovered that I can’t even do superscripts in this particular WordPress style), but the entire Mathematica notebook with the proof is available if you want it.  This is what they show: