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

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 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.

SUBMITTER: LaFleur TL 

PROVIDER: S-EPMC9440211 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature