It’s difficult for bioinformaticians to publish in so-called “high impact” journals at all, never mind as first author. Many of us are not group leaders with tightly-defined research programs; we are the “go to” guys, happy to apply our skills to any dataset that comes our way. In academia at least, we’re caught somewhere between research scientist and IT support. It can be a frustrating life.
So it’s good to see that Nature, a journal not renowned for publishing articles with a strong computational biology component, has seen fit to publish this:
It’s a fascinating piece of work. The authors started with an enzyme of unknown activity from Thermotoga maritima, a thermophilic bacterium. Initial structure/sequence analysis suggested a superfamily for the enzyme. They then compiled a list of ~ 4 000 potential substrates, modelled tetrahedral intermediates that could resemble the transition state for each one and performed docking simulations of each model with a model of the enzyme active site. This generated 4 candidate substrates, 3 of which were confirmed by biochemical assay. For a finale, they determined a crystal structure for the enzyme + bound product which agreed closely with the model and identified a new metabolic pathway for orthologues of the enzyme in other genomes.
There are a few criticisms of the approach. This is a case where modelling was a good approximation to reality, but there are plenty of cases where that isn’t true. It also relies on quite a lot of prior knowledge concerning sequence/structure and metabolism, which isn’t available for many uncharacterised proteins. And it’s currently quite computationally expensive, so not exactly high-throughput, although that will change of course. Still, if you enjoy genome-scale bioinformatics and structural biology, it’s almost enough to make you drool.