Automated model-predictive design of synthetic promoters to control transcriptional profiles in bacteria.

Autor: LaFleur TL; Department of Chemical Engineering, Pennsylvania State University, University Park, PA, 16801, USA., Hossain A; Bioinformatics and Genomics, Pennsylvania State University, University Park, PA, 16801, USA., Salis HM; Department of Chemical Engineering, Pennsylvania State University, University Park, PA, 16801, USA. salis@psu.edu.; Bioinformatics and Genomics, Pennsylvania State University, University Park, PA, 16801, USA. salis@psu.edu.; Department of Biological Engineering, Pennsylvania State University, University Park, PA, 16801, USA. salis@psu.edu.; Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, 16801, USA. salis@psu.edu.
Jazyk: angličtina
Zdroj: Nature communications [Nat Commun] 2022 Sep 02; Vol. 13 (1), pp. 5159. Date of Electronic Publication: 2022 Sep 02.
DOI: 10.1038/s41467-022-32829-5
Abstrakt: 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 70 promoter sequence, validated across 22132 bacterial promoters with diverse sequences. We apply the model to predict genetic context effects, design σ 70 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.
(© 2022. The Author(s).)
Databáze: MEDLINE
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