biostudies-literatureLaFleur TLUnited States Department of Defense | Defense Advanced Research Projects Agency (DARPA)U.S. Department of Energy (DOE)U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)National Science Foundation (NSF)5159https://www.ebi.ac.uk/biostudies/studies/S-EPMC9440211biostudies-literatureUnknown13(1)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 σ⁷⁰ promoter sequence, validated across 22132 bacterial promoters with diverse sequences. We apply the model to predict genetic context effects, design σ⁷⁰ 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.Nature communicationsAutomated model-predictive design of synthetic promoters to control transcriptional profiles in bacteria.PMC94402111T32GM102057HR001117C0095MCB-2131923DE-SC0019090Hossain ASalis HMLaFleur TLfalseAutomated model-predictive design of synthetic promoters to control transcriptional profiles in bacteria.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 σ⁷⁰ promoter sequence, validated across 22132 bacterial promoters with diverse sequences. We apply the model to predict genetic context effects, design σ⁷⁰ 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.2022-01-01T00:00:00Z2022 Sep2025-04-04T08:42:03.196Z2025-04-04T08:42:03.196ZS-EPMC94402113605602910.1038/s41467-022-32829-5