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

LaFleur, Travis L.; Hossain, Ayaan; Salis, Howard M.

doi:10.1038/s41467-022-32829-5

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

Journal Article · 01 September 2022 · Nature Communications

DOI: https://doi.org/10.1038/s41467-022-32829-5 · OSTI ID:1885200

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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 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.

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Research Organization:: Pennsylvania State Univ., University Park, PA (United States)

Sponsoring Organization:: Defense Advanced Research Projects Agency (DARPA); National Institutes of Health (NIH); National Science Foundation (NSF); USDOE; USDOE Office of Science (SC)

Grant/Contract Number:: SC0019090

OSTI ID:: 1885200

Journal Information:: Nature Communications, Journal Name: Nature Communications Journal Issue: 1 Vol. 13; ISSN 2041-1723

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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42 ENGINEERING
59 BASIC BIOLOGICAL SCIENCES
machine learning
synthetic biology
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Title: Automated model-predictive design of synthetic promoters to control transcriptional profiles in bacteria

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