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A deep learning approach to programmable RNA switches

Author

Listed:
  • Nicolaas M. Angenent-Mari

    (Massachusetts Institute of Technology (MIT)
    MIT
    Harvard University)

  • Alexander S. Garruss

    (Harvard University
    Harvard University
    Harvard Medical School)

  • Luis R. Soenksen

    (Massachusetts Institute of Technology (MIT)
    MIT
    Harvard University
    MIT)

  • George Church

    (Harvard University
    Harvard Medical School
    Harvard-MIT Program in Health Sciences and Technology)

  • James J. Collins

    (Massachusetts Institute of Technology (MIT)
    MIT
    Harvard University
    MIT)

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.

Suggested Citation

  • Nicolaas M. Angenent-Mari & Alexander S. Garruss & Luis R. Soenksen & George Church & James J. Collins, 2020. "A deep learning approach to programmable RNA switches," Nature Communications, Nature, vol. 11(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18677-1
    DOI: 10.1038/s41467-020-18677-1
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    Cited by:

    1. Raphaël V. Gayet & Katherine Ilia & Shiva Razavi & Nathaniel D. Tippens & Makoto A. Lalwani & Kehan Zhang & Jack X. Chen & Jonathan C. Chen & Jose Vargas-Asencio & James J. Collins, 2023. "Autocatalytic base editing for RNA-responsive translational control," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    2. SJ, Balaji & Babu, Suresh Chandra & Pal, Suresh, 2021. "Understanding Science and Policy Making in Agriculture: A Machine Learning Application for India," 2021 Conference, August 17-31, 2021, Virtual 315227, International Association of Agricultural Economists.
    3. Naoki Hayashi & Yong Lai & Jay Fuerte-Stone & Mark Mimee & Timothy K. Lu, 2024. "Cas9-assisted biological containment of a genetically engineered human commensal bacterium and genetic elements," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    4. Evangelos-Marios Nikolados & Arin Wongprommoon & Oisin Mac Aodha & Guillaume Cambray & Diego A. Oyarzún, 2022. "Accuracy and data efficiency in deep learning models of protein expression," Nature Communications, Nature, vol. 13(1), pages 1-12, December.

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