Edited by David Baker, University of Washington, Seattle, WA, and approved December 12, 2013 (received for review July 12, 2013)
Self-assembling RNA molecules play critical roles throughout biology and bioengineering. To accelerate progress in RNA design, we present EteRNA, the first internet-scale citizen science “game” scored by high-throughput experiments. A community of 37,000 nonexperts leveraged continuous remote laboratory feedback to learn new design rules that substantially improve the experimental accuracy of RNA structure designs. These rules, distilled by machine learning into a new automated algorithm EteRNABot, also significantly outperform prior algorithms in a gauntlet of independent tests. These results show that an online community can carry out large-scale experiments, hypothesis generation, and algorithm design to create practical advances in empirical science.
Self-assembling RNA molecules present compelling substrates for the rational interrogation and control of living systems. However, imperfect in silico models—even at the secondary structure level—hinder the design of new RNAs that function properly when synthesized. Here, we present a unique and potentially general approach to such empirical problems: the Massive Open Laboratory. The EteRNA project connects 37,000 enthusiasts to RNA design puzzles through an online interface. Uniquely, EteRNA participants not only manipulate simulated molecules but also control a remote experimental pipeline for high-throughput RNA synthesis and structure mapping. We show herein that the EteRNA community leveraged dozens of cycles of continuous wet laboratory feedback to learn strategies for solving in vitro RNA design problems on which automated methods fail. The top strategies—including several previously unrecognized negative design rules—were distilled by machine learning into an algorithm, EteRNABot. Over a rigorous 1-y testing phase, both the EteRNA community and EteRNABot significantly outperformed prior algorithms in a dozen RNA secondary structure design tests, including the creation of dendrimer-like structures and scaffolds for small molecule sensors. These results show that an online community can carry out large-scale experiments, hypothesis generation, and algorithm design to create practical advances in empirical science.
↵2A complete list of the EteRNA Group can be found in Dataset S1.
Author contributions: J.L., M.A., S.Y., A.T., R.D., and EteRNA Participants designed research; J.L., W.K., M.L., D.C., M.A., H.K., A.L., S.Y., A.T., R.D., and EteRNA Participants performed research; J.L., W.K., M.L., D.C., M.A., H.K., A.L., S.Y., A.T., and EteRNA Participants analyzed data; and J.L., A.T., and R.D. wrote the paper.
Conflict of interest statement: The editor, David Baker, is a recent coauthor with R.D. having published a paper with him in 2013.
This article is a PNAS Direct Submission.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1313039111/-/DCSupplemental.
Freely available online through the PNAS open access option.
