Self-assembling RNA molecules present compelling substrates for rational interrogation, control, and therapeutic intervention in living systems and disease. Computational methods for designing and predicting RNA secondary structures, switches, and 3D folds are playing fundamental roles in a new generation of RNA devices and therapeutics. However, imperfect <i>in silico</i> models – even at the secondary structure level – hinder the design of new RNAs that function properly when synthesized. This work presents a novel and potentially general approach to such empirical problems, the Massive Open Laboratory. The EteRNA project has connected 37,000 enthusiasts to RNA design ‘puzzles’ via an on-line interface. Uniquely, EteRNA participants not only manipulate simulated molecules, but also control a remote experimental pipeline for high-throughput RNA synthesis and structure mapping. Our initial results demonstrate how the EteRNA community leveraged dozens of cycles of continuous wet-lab feedback to learn strategies for solving <i>in vitro </i>RNA design problems on which automated methods fail. The top strategies – including several previously unrecognized ‘negative design rules’ – were distilled by machine learning into a new algorithm, EteRNABot. Over a rigorous one-year 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 demonstrate for the first time that an online community can carry out large-scale experiments, hypothesis generation, and algorithm design to create practical advances in empirical science.

RNA design rules through internet-scale social computing and high-throughput chemistry

249th National Meeting (2015)

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