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bioRxiv · 10.1101/2021.09.01.458561

Automated Model-Predictive Design of Synthetic Promoters to Control Transcriptional Profiles in Bacteria

La Fleur, T. L.·Hossain, A.·Salis, H. M.
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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 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.

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La Fleur, T. L., Hossain, A., Salis, H. M.. 2021-09-01. Automated Model-Predictive Design of Synthetic Promoters to Control Transcriptional Profiles in Bacteria. https://doi.org/10.1101/2021.09.01.458561

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