EternaBrain: Automated RNA design through move sets and strategies from an Internet-scale RNA videogame

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.

Redesigning the Eterna100 for the Vienna 2 folding engine

“…On the Vienna 2modified secondary structures of Eterna100-V2, EternaBrain and SentRNA both solved fewer puzzles: EternaBrain solving 1 and SentRNA solving 3. This was expected, as both inferred Vienna 1 solving strategies, either learned via neural networks or via explicitly encoded strategies in the algorithms [24]. Similarly, LEARNA solved four puzzles, two more than its Vienna 1 performance.…”

“…EternaBrain uses a combination of a convolutional neural network trained on Eterna player moves and a Single Action Playout (SAP) [24], a depth-1 Monte Carlo Search using Eterna player strategies. To adapt EternaBrain to Vienna 2, the folding engine in the SAP was changed to Vienna 2.…”

“…This Eterna100 benchmark was chosen to showcase secondary structure motifs that were identified as being difficult to design, and the best performing algorithm [21] solved 54/100. Since the benchmark was published, several algorithms have surpassed this mark using convolutional neural networks [24], reinforcement learning [25,26], or a Monte Carlo search optimized for game theory [27]. However, algorithms have been inconsistent in which folding engine (i.e., secondary structure prediction algorithm) they use both in training and in predicting.…”

“…However, algorithms have been inconsistent in which folding engine (i.e., secondary structure prediction algorithm) they use both in training and in predicting. For instance, EternaBrain [24] used Vienna 1.8, but Meta-LEARNA [26] used the Turner 2004 thermodynamic parameters in Vienna 2.1.8. Despite the fundamental link between the folding engine used to run and evaluate inverse folding algorithms, there has been no systematic evaluation of the effect of folding engines in the training and performance of inverse folding algorithms.…”

RNA secondary structure prediction with Convolutional Neural Networks

“…Learning-based methods dominate in related structure prediction problems. For example EternaBrain [17] uses reinforcement learning to address the RNA sequence design (inverse folding) problem. As another example, the recently proposed AlphaFold [18] set the new state of the art in predicting structures for proteins.…”