PT  - JOURNAL ARTICLE
AU  - Rohan V. Koodli
AU  - Benjamin Keep
AU  - Katherine R. Coppess
AU  - Fernando Portela
AU  - Eterna players
AU  - Rhiju Das
TI  - EternaBrain: Automated RNA design through move sets from an Internet-scale RNA videogame
AID  - 10.1101/326736
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 326736
4099  - http://biorxiv.org/content/early/2018/05/21/326736.short
4100  - http://biorxiv.org/content/early/2018/05/21/326736.full
AB  - Folded RNA molecules underlie emerging approaches to disease detection and gene therapy. These applications require RNA sequences that fold into target base-pairing patterns, but computational algorithms generally remain inadequate for these RNA secondary structure design tasks. The Eterna project has collected over 1 million player moves by crowdsourcing RNA designs in the form of puzzles that reach extraordinary difficulty. Here, we present these data in the eternamoves repository and test their utility in training a multilayer convolutional neural network to predict moves. When pipelined with hand-coded move combinations developed by the Eterna community, the resulting EternaBrain method solves 61 out of 100 independent RNA design puzzles in the Eterna100 benchmark. EternaBrain surpasses all six other prior algorithms that were not informed by Eterna strategies and suggests a path for automated RNA design to achieve human-competitive performance.