Automated 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 combined massively parallel assays, biophysics, and machine learning to develop a 346-parameter model that predicts site-specific transcription initiation rates for any {sigma}70 promoter sequence, validated across 17396 bacterial promoters with diverse sequences. We applied the model to predict genetic context effects, design {sigma}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. One-Sentence SummaryA 346-parameter model predicted DNAs interactions with RNA polymerase initiation complex, enabling accurate transcription rate predictions and automated promoter design in bacterial genetic systems.