{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["LaFleur TL"],"funding":["United 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)"],"pagination":["5159"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9440211"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["13(1)"],"pubmed_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."],"journal":["Nature communications"],"pubmed_title":["Automated model-predictive design of synthetic promoters to control transcriptional profiles in bacteria."],"pmcid":["PMC9440211"],"funding_grant_id":["1T32GM102057","HR001117C0095","MCB-2131923","DE-SC0019090"],"pubmed_authors":["Hossain A","Salis HM","LaFleur TL"],"additional_accession":[]},"is_claimable":false,"name":"Automated model-predictive design of synthetic promoters to control transcriptional profiles in bacteria.","description":"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.","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Sep","modification":"2025-04-04T08:42:03.196Z","creation":"2025-04-04T08:42:03.196Z"},"accession":"S-EPMC9440211","cross_references":{"pubmed":["36056029"],"doi":["10.1038/s41467-022-32829-5"]}}