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Big data tackles tiny molecular machines
Date:3/14/2014

s open to release the peptides and the molecule expands again to restart the cycle."

Key to the success of DCA is the realization that amino acid mutations represent contacts that co-evolve for specific purposes. The contact maps created by DCA can reveal previously unknown details to model transitions between functional states, like the paddling in FtsH, Onuchic said.

"We can look at the evolutionary tree of these proteins and see which pairs of amino acids changed together. We then assume these are contacts," he said. "Through DCA, Faruck uses a lot of physics to understand when two amino acids can act directly or indirectly, and separate the two. Then we predict how coupled they are, and the higher the probability, the more evidence that these are real contacts."

DCA would do little without the flood of data available since the ability to scan entire genomes became possible, and even commonplace, in recent decades. Advances in the century-old art of crystallography are making better structure-based models available as well.

"Even if the mathematical framework was ready and we had crystallographic data for this motor protein in the 1990s, there weren't enough sequences available until the 2000s," Morcos said. "Now we have all the pieces converging."

He said understanding essential motor proteins in bacteria will be important as researchers begin to apply DCA to advance human health. "For us, the most exciting part is that we're now able to tackle really big systems," Morcos said.


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Contact: David Ruth
david@rice.edu
713-348-6327
Rice University
Source:Eurekalert  

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