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Abstract
Emerging RNA-based approaches to disease detection and gene therapy require RNA sequences that fold into specific base-pairing patterns, but computational algorithms generally remain inadequate for these secondary structure design tasks. The Eterna project has crowdsourced RNA design to human video game players in the form of puzzles that reach extraordinary difficulty. Here, we demonstrate that Eterna participants’ moves and strategies can be leveraged to improve automated computational RNA design. We present an eternamoves-large repository consisting of 1.8 million of player moves on 12 of the most-played Eterna puzzles as well as an eternamoves-select repository of 30,477 moves from the top 72 players on a select set of more advanced puzzles. On eternamoves-select, we present a multilayer convolutional neural network (CNN) EternaBrain that achieves test accuracies of 51% and 34% in base prediction and location prediction, respectively, suggesting that top players’ moves are partially stereotyped. Pipelining this CNN’s move predictions with single-action-playout (SAP) of six strategies compiled by human players solves 61 out of 100 independent puzzles in the Eterna100 benchmark. EternaBrain-SAP outperforms previously published RNA design algorithms and achieves similar or better performance than a newer generation of deep learning methods, while being largely orthogonal to these other methods. Our study provides useful lessons for future efforts to achieve human-competitive performance with automated RNA design algorithms.
Highlights
Due to its versatility and important roles throughout biology, there is strong interest in designing RNA-guided machines for disease detection, virus defense, and gene therapy, e.g., for gene silencing and CRISPR/Cas9 gene editing [1][2]
The design of RNA sequences that fold into target structures is a computationally difficult task whose importance continues to grow with the advent of RNA-based therapeutics and diagnostics
EternaBrain: Automated RNA design from an Internet-scale RNA videogame events/discovery-innovation-awards.html/ The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
Summary
Due to its versatility and important roles throughout biology, there is strong interest in designing RNA-guided machines for disease detection, virus defense, and gene therapy, e.g., for gene silencing and CRISPR/Cas gene editing [1][2]. Advanced participants can submit their RNA designs in lab challenges and receive wet-lab feedback on how their molecules fold during in vitro experiments performed on a weekly time scale. These efforts expose the ‘reality gap’ in RNA design–the mismatch between current computational folding models and experiment. In preparation for these experimental challenges, participants challenge fellow participants through in silico puzzles that require learning or developing sophisticated puzzle-solving strategies; these separate ‘games within the game’ are useful for guiding the development of computational RNA design methods [10]. This community has been successful in designing RNAs that consistently outperform RNA design algorithms in both in silico and in vitro tests [10]
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