Crowdsourced RNA designs outperform computer algorithms

An enthusiastic group of non-experts, working through an online interface and receiving feedback from lab experiments, has produced designs for RNA molecules that are consistently more successful than those generated by the best computerized design algorithms, researchers report.

Moreover, the researchers gathered some of the best design rules and practices generated by players of the online EteRNA design challenge and, using machine learning principles, generated their own automated design algorithm, EteRNABot, which also bested prior design algorithms. Though this improved computer design tool is faster than humans, the designs it generates still don't match the quality of those of the online community, which now has more than 130,000 members.

The research will be published this week in the Proceedings of the National Academy of Sciences Online Early Edition.

"The quality of the designs produced by the online EteRNA community is just amazing and far beyond what any of us anticipated when we began this project three years ago," said Adrien Treiulle, an assistant professor of computer science and robotics at Carnegie Mellon, who leads the project with Rhiju Das, an assistant professor of biochemistry at Stanford, and Jeehyung Lee, a Ph.D. student in computer science at Carnegie Mellon.

"This wouldn't be possible if EteRNA members were just spitting out designs using online simulation tools," Treuille continued. "By actually synthesizing the most promising designs in Das' lab at Stanford, we're giving our community feedback about what works and doesn't work in the physical world. And, as a result, these non-experts are providing us insight into RNA design that is significantly advancing the science."