SentRNA: Improving computational RNA design by incorporating a prior of human design strategies
This addresses a bottleneck in RNA design for bioengineering applications like catalysis and therapy, representing a new paradigm rather than an incremental improvement.
The authors tackled the RNA inverse folding problem by developing SentRNA, a neural network trained on human-designed sequences, which solved previously unsolvable complex targets and achieved state-of-the-art performance on challenging test sets.
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.