A deep learning approach to programmable RNA switches

Nicolaas M. Angenent-Mari, Alexander S. Garruss, Luis R. Soenksen, George Church and James J. Collins ()
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Nicolaas M. Angenent-Mari: Massachusetts Institute of Technology (MIT)
Alexander S. Garruss: Harvard University
Luis R. Soenksen: Massachusetts Institute of Technology (MIT)
George Church: Harvard University
James J. Collins: Massachusetts Institute of Technology (MIT)

Nature Communications, 2020, vol. 11, issue 1, 1-12

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-18677-1

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