Sep 112007
 
I hate the idea of submitting a paper to Proceedings of the National Academy of Sciences because if you get rejected you feel especially bad. Despite its relatively high impact factor, PNAS puts out a fair amount of garbage, and sometimes you feel like you’re reading a dumping ground for the bad ideas of big names. Case in point is a paper from the lab of Eugene Shaknovich that just showed up in pre-publication form. Shaknovich is famous for performing simulations of proteins that rely on a reduced or “lattice” representation, and in the interest of full disclosure I’ll admit that I’m highly dubious of this method. In this case, however, I’m relatively certain that the simulation produced a correct answer, for reasons that should become obvious as we go along.

Deeds et al. are out to address what seems like a fairly interesting problem of specificity in protein interactions. Most experiments performed in biomolecular laboratories make use of highly overexpressed proteins in relatively pure environments. This allows us to obtain quality information about the details of particular proteins and their interactions. However, what happens when we put these proteins into the cell at relatively dilute concentrations? If we consider a particular pair of interacting proteins, A and B, we know that their specific binding is strongly energetically favored. However, A and B probably have the potential to interact nonspecifically with many other proteins. If A and B are at relatively low concentration, and the cellular milieu is crowded with millions of these potential nonspecific interactions, can we be sure A and B will find one another?

To address this question, Deeds et al. construct their typical lattice models with two “proteins” that have a designed interaction, and simulated a situation in which up to 90% of a 3d space is populated by random “proteins” that have a small potential to interact with the targets. They let the systems equilibrate, and then take a look at the kind of interactions that are occurring. The findings are truly revolutionary, as you can see from the figure below. At low temperatures (A), the random interactions (blue) predominate when nonspecific-binding proteins are the larger component of the system. As the temperature increases slightly, to a point above the ‘melting temperature’ of random complexes, the designed, ‘specific’ interactions (black) begin to play a larger role, even at low ‘concentrations’ of interacting proteins. Finally, as the temperature really climbs, the specific interactions start to dominate the milieu.


So basically what Deeds et al. have discovered is that when the energy available from heat is enough to break random interactions but not specific interactions, then at equilibrium specific interactions are highly favored even if there is an awful lot of opportunity to bind randomly. On the other hand, if the temperature of the system is too low to break up random-binding events, then random binding dominates when most of the proteins are nonspecific binders. This is not a surprise, and hardly qualifies as a conclusion at all, even less so when you consider that this is a simulation (and thus that unsurprising results are likely to result directly from the assumptions used) and not an experiment on real proteins. This seems more like a control for some other experiment using this same system that derives some surprising conclusion.

Deeds et al. go on to make some important predictions from their discovery that energetics dictate binding (who knew kT could be so important?). For instance, they point out that in crowded protein-protein interaction experiments like the yeast two-hybrid screen there may be a lot of false positives, which must come as shocking news to any yeast two-hybrid researchers who have never read any papers or protocols about yeast two-hybrid screens, nor even Wikipedia. They also recommend using techniques that don’t rely on overexpression, but the fact is that almost any molecular biologist would prefer it if he could answer questions using experiments that involved no overexpression. None of this is new, and to treat the insight as novel because some simulation showed it is just insulting.

In the end, this is a paper about a control, and not even a particularly well-written one. Don’t get me wrong; it isn’t bad for what it is, and I don’t blame Shaknovich for trying to get it published in the best journal he could find for it. This is not bad science, but it doesn’t tell us anything new, doesn’t present a novel technique with wide application, or really provide any other reason to appear in a broad-based journal. I can and do blame PNAS for allowing in something that really ought to have been relegated to a specialized journal. And I will blame Shaknovich for the condescending tone of the discussion, in which he descends from his lofty computational height to tell us poor experimentalists “interesting implications” that we already knew. Too often, simulations get a bad rap from experimentalists, but this sort of paper, which will leave a bad taste in the mouth of anyone unfortunate enough to spend time reading it, makes that attitude seem justified.

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