Bosco Ho describes the most important findings from the past decade in computational structural biology and imparts this fascinating endeavor of which I was quite unaware:
Every 2 years, a whole bunch of computational structural biology labs effectively shut down for business, and throw every man, woman and workstation together to attempt to crack a set of problems. This same set of problems is simultaneously being attempted in labs all around the world, as researchers race against a clock to predict the 3 dimensional atomic structure of protein sequences published at the CASP protein-folding competition website.
We often say that science is a competition but is is astonishing to me how the computational structural biology community has embraced formally organized competitions such as CASP. Here, we have pure naked competition, complete with a scoring system, judges, and rankings that determine winners and losers. It has all the drama that you’d expect from a reality TV show: recriminations, anger and tears. And it has taken the field of protein folding much farther than anyone would have imagined 10 years ago…
The field of protein folding had been drifting along in some kind of crappy fitness valley and it wasn’t until CASP came along, that we could even define what protein folding was, in a concrete definitive way. In terms of protein folding, the targets of CASP could be used to define a good fitness function, which was enough to spur the field to scramble out of the valley and up a fitness peak.
Here’s a similar competition that one might design for in vitro anatomical neuroscience. Have some central organization, analogous to the CASP, decide on a set of cultured neurons to analyze.* The org would then perform a few tests on the cultured neurons to determine a few variables, including chemical analysis to determine the types of neurotransmitters used, western blotting / optical density readings to determine the relevant protein densities at each synapse, some sort of microscopy from various angles to determine cell shape and type, etc. The org would then perform various electrical inputs to each of the neurons at controlled time sequences and measure the outputs, but crucially, would not make this output data public. Competitors would be given the anatomical data and the magnitude of the electrical inputs and attempt to predict the output and activity of each neuron on this basis. This could help test and refine neuron models. What say ye? Doable?
* Probably start small in terms of numbers and in terms of neural complexity. They wouldn’t want too many endogenous oscillator neurons in the first few iterations!