Automated model-predictive design of synthetic promoters to control transcriptional profiles in bacteria.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 36056029.
- Also identified by DOI 10.1038/s41467-022-32829-5 and PMC identifier 9440211.
- 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
Transcription rates are regulated by the interactions between RNA polymerase, sigma factor, and promoter DNA sequences in bacteria. However, it remains unclear how non-canonical sequence motifs collectively control transcription rates. Here, we combine massively parallel assays, biophysics, and machine learning to develop a 346-parameter model that predicts site-specific transcription initiation rates for any σ<sup>70</sup> promoter sequence, validated across 22132 bacterial promoters with diverse sequences. We apply the model to predict genetic context effects, design σ<sup>70</sup> promoters with desired transcription rates, and identify undesired promoters inside engineered genetic systems. The model provides a biophysical basis for understanding gene regulation in natural genetic systems and precise transcriptional control for engineering synthetic genetic systems.
Medical subject headings
- DNA-Directed RNA Polymerases
- Sigma Factor