Designing RNA sequences that fold into specific structures and perform desired biological functions is an emerging field in bioengineering with broad applications from intracellular chemical catalysis to cancer therapy via selective gene silencing. Effective RNA design requires first solving the inverse folding problem: given a target structure, propose a sequence that folds into that structure. Although significant progress has been made in developing computational algorithmsfor this purpose , current approaches are ineffective at designing sequences for complex targets, limiting their utility in real-world applications. However, an alternative that has shown significantly higher performance are human players of the online RNA design game EteRNA . Through many rounds of gameplay, these players have developed a collective library of "human" rules and strategies for RNA design that have proven to be more effective than current computational approaches, especially for complex targets . Here, we present an RNA design agent , SentRNA, which consists of a fully-connected neural network trained using the eternasolves dataset, a set of 1.8 x 10^4 player-submitted sequencesacross 724 unique targets. The agent first predicts an initial sequence for a target using the trained network, and then refines that solution if necessary using a short adaptive walk utilizing a canon of standard design moves. Through this approach, we observe SentRNA can learn and apply human-like design strategies to solve several complex targets previously unsolvable by any computational approach . We thus demonstrate that incorporating a prior of human design strategies into a computational agent can significantly boost its performance , and suggests a new paradigm for machine-based RNA design.
Solving the RNA inverse folding problem is a critical prerequisite to RNA design, an emerging field in bioengineering with a broad range of applications from reaction catalysis to cancer therapy . Although significant progress has been made in developing machine-based inverse RNA folding algorithms , current approaches still have difficulty designing sequences for large or complex targets. On the other hand, human players of the online RNA design game EteRNA have consistently shown superior performance in this regard, being able to readily design sequences for targets that are challenging for machine algorithms. Here we present a novel approach to the RNA design problem , SentRNA, a design agent consisting of a fully-connected neural network trained end-to-end using human-designed RNA sequences. We show that through this approach, SentRNA can solve complex targets previously unsolvable by any machine-based approach and achieve state-of-the-art performance on two separate challenging test sets. Our results demonstrate that incorporating human design strategies into a design algorithm can significantly boost machine performance and suggests a new paradigm for machine-based RNA design.
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using the eternasolves dataset, a set of 1.8 x 10^4 player-submitted sequencesacross 724 unique targets. The agent first predicts an initial sequence for a target using the trained network, and then refines that solution if necessary using a short adaptive walk utilizing a canon of standard design moves. Through
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