Scientific Method / Science & Exploration

Massive Open Lab gets volunteers to beat computers in designing RNA

Gamers' designs turned into real molecules, and work better than computers'.

Enlarge / Your RNA here. The eterna interface makes it a snap to get your own custom RNA to form a specific structure.

Citizen science, the movement to draft non-specialists into areas of scientific research, doesn't require the volunteers to put on lab coats. In at least one case, scientists turned a prickly biochemical problem into a game and found that the gamers could typically beat the best computer algorithms out there.

But all that work was done on cases where we already knew the answers, which was how we were able to measure the gamers' success. Now some researchers have taken this approach one step further and created a hybrid project that mixes volunteers with lab-coated workers. 37,000 enthusiasts were given the chance to take on algorithms in designing new RNA molecules. And once the gamers had a chance to vote on the best designs, the winning designs were sent to a lab, synthesized, and tested. After a few rounds of this, players were not only handily beating the computers but providing rules that went into designing the next-generation algorithm.




In the first round, the current state-of-the-art software did a better job of designing RNA structures than the players did. That didn't last, though. "As the community gained experience with empirical RNA design cycles," the authors write, "performance improved, and community submissions converged to successful designs." By the third challenge, the best player-contributed designs were outperforming the best algorithms; by the sixth round, the median player design was better than the best that the computers could come up with.

The system also allowed the users to create simple rules to help them design effective structures (things like "put a G-C pair at the base of a stem"). The authors checked these and found that many of the design principles hadn't ever been identified in the scientific literature. So they designed an algorithm that could incorporate the user-designed rules. That algorithm also beat the previous state-of-the-art and often approached the best of the human designs.

From here, the researchers behind the Massive Open Lab plan on taking the work in two directions. They intend to try to figure out why, at the biochemical level, the players' rules result in a more stable RNA structure. And, at the same time, they want to set their players onto designing more complicated structures—ones that could bind a specific molecule or perform a catalytic reaction.

PNAS, 2014. DOI: 10.1073/pnas.1313039111  (About DOIs).

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