New Videogame Lets Amateur Researchers Mess With RNA

[n2doc]

Jessica Fournier has a job that makes poor use of her talents. She spends her days stocking sneakers at a warehouse outside Grand Rapids, Michigan. A decade ago she was an astrophysics student at Michigan State University, where she coauthored a paper on RR Lyrae, a low-mass star that pulsates light. But having failed to secure long-term employment in her arcane field, today she pays her bills by cataloging shoe sizes.

She may have given up astrophysics, but Fournier still has a deep love of science. As soon as she gets home from work each night, she boots up her Asus laptop and begins what she calls “my second job”: designing molecules of ribonucleic acid—RNA—that have the power to build proteins or regulate genes. It is a job that she happens to perform better than almost anyone else on earth.

Under the fitting nickname “starryjess,” Fournier is the world’s second-ranked player of EteRNA, an online game with more than 38,000 registered users. Featuring an array of clickable candy-colored pieces, EteRNA looks a little like the popular game Bejeweled. But instead of combining jewel shapes in Tetris-like levels, EteRNA players manipulate nucleotides, the fundamental building blocks of RNA, to coax molecules into shapes specified by the game. Those shapes, which typically look like haphazardly mowed crop circles or jumbled chain-link necklaces, represent how RNA appears in nature while it goes about its work as one of life’s most essential ingredients. No self-sustaining organism gets made without the involvement of RNA.

Tweaking molecular models in this fashion is surprisingly fun—and, it turns out, useful. EteRNA was developed by scientists at Stanford and Carnegie Mellon universities, who use the designs created by players to decipher how real RNA works. The game is a direct descendant of Foldit—another science crowdsourcing tool disguised as entertainment—which gets players to help figure out the folding structures of proteins. EteRNA, though, goes much further than its predecessor.

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http://www.wired.com/wiredscience/2012/07/ff_rnagame/

[Ohio Joe]

1. Cool stuff

I'm going to post a link to this in the gaming group. Thanks!

[phantom power]

2. This passage is interesting - crowdsourcing deep science insight:

What did catch EteRNA‘s creators off guard, though, was the players’ knack for identifying and defining the rules of RNA behavior. It is one thing for amateurs to help solve a tricky puzzle; quite another for them to discern important laws of nature—the kind of thing that could conceivably earn a student a PhD—then share their discoveries with the world.

After several weeks of studying EteRNA‘s winning designs, for example, Fisker noticed that the most successful molecules were those that placed guanine-cytosine pairs at the junctions where multiple straight stems of nucleotides feed into massive loops. But simply aping this strategy in his designs didn’t always work as he envisioned. Fisker was missing something.

And then it dawned on him: Orientation meant everything. Unless the guanine- cytosine pairs all faced in the same direction, so that red always alternated with green as you looked around the loop, the molecule wouldn’t form correctly when synthesized. This breakthrough soon turned Fisker’s designs into world-beaters—and, more important, enhanced biochemistry’s grasp of RNA. It was a fundamental rule that no scientist had ever discovered before.

Several other EteRNA champions have also uncovered major properties of RNA. Jessica Fournier, for example, was among a handful of players to discover the importance of variation in a certain type of molecule. “If the design has a lot of arms that are the same length, try not to make them too alike, or they could mispair,” she explains. This is because nucleotides continue to attract and repel one another even when they are separated by relatively large distances. Making sure those rows have little in common—that the nucleotide sequence of each is distinct—minimizes the risk of having a base pair pulled out of alignment.

Das didn’t anticipate this kind of discovery. “The pattern recognition stuff, OK, I knew people were going to be good at that from the experience we had with Foldit,” he says. “What I didn’t know was how they were going to interpret and use the data. But if you look at what they’re doing, it’s much better work than some of the best graduate-level scientists. What they’re doing, it’s really beautiful.”

[Dred]

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