A deep learning approach to programmable RNA switches.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 33028812.
- Also identified by DOI 10.1038/s41467-020-18677-1 and PMC identifier 7541447.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
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 (R<sup>2</sup> = 0.43-0.70) previous state-of-the-art thermodynamic and kinetic models (R<sup>2</sup> = 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.
Medical subject headings
- Deep Learning
- Genetic Engineering
- Riboswitch
- Synthetic Biology