Ribozyme switches are a class of RNA-encoded genetic switch that support conditional regulation of gene expression across diverse organisms. An improved elucidation of the relationships between sequence, structure, and activity can improve our capacity for de novo rational design of ribozyme switches. Here, we generated data on the activity of hundreds of thousands of ribozyme sequences. Using automated structural analysis and machine learning, we leveraged these large datasets to develop predictive models that estimate the in vivo gene-regulatory activity of a ribozyme sequence. These models supported the de novo design of ribozyme libraries with low mean basal gene-regulatory activities and new ribozyme switches that exhibit changes in gene-regulatory activity in the presence of a target ligand, producing functional switches for four out of five aptamers. Our work examines how biases in the model and the dataset that affect prediction accuracy can arise and demonstrates that machine learning can be applied to RNA sequences to predict gene-regulatory activity, providing the basis for design tools for functional RNAs.
All data generated or analyzed during this study and including in the manuscript and supporting file. Source data files are provided where appropriate.
High-throughput cellular RNA device engineeringSource Data for Figure 1 and 3.
- Christina D Smolke
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
- Detlef Weigel, Max Planck Institute for Developmental Biology, Germany
- Received: June 5, 2020
- Accepted: April 15, 2021
- Accepted Manuscript published: April 16, 2021 (version 1)
© 2021, Schmidt & Smolke
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