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A deep learning approach to programmable RNA switches.

ABSTRACT: Engineered RNA elements are programmable tools capable of detecting small molecules, proteins, and nucleic acids. Predicting the behavior of these synthetic biology components remains a challenge, a situation that could be addressed through enhanced pattern recognition from deep learning. Here, we investigate Deep Neural Networks (DNN) to predict toehold switch function as a canonical riboswitch model in synthetic biology. To facilitate DNN training, we synthesize and characterize in vivo a dataset of 91,534 toehold switches spanning 23 viral genomes and 906 human transcription factors. DNNs trained on nucleotide sequences outperform (R2?=?0.43-0.70) previous state-of-the-art thermodynamic and kinetic models (R2?=?0.04-0.15) and allow for human-understandable attention-visualizations (VIS4Map) to identify success and failure modes. This work shows that deep learning approaches can be used for functionality predictions and insight generation in RNA synthetic biology.

SUBMITTER: Angenent-Mari NM

PROVIDER: S-EPMC7541447 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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A deep learning approach to programmable RNA switches.

Angenent-Mari Nicolaas M NM Garruss Alexander S AS Soenksen Luis R LR Church George G Collins James J JJ

Nature communications 20201007 1

Engineered RNA elements are programmable tools capable of detecting small molecules, proteins, and nucleic acids. Predicting the behavior of these synthetic biology components remains a challenge, a situation that could be addressed through enhanced pattern recognition from deep learning. Here, we investigate Deep Neural Networks (DNN) to predict toehold switch function as a canonical riboswitch model in synthetic biology. To facilitate DNN training, we synthesize and characterize in vivo a data ...[more]

PMID: 33028812

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