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

Travis La Fleur, View ORCID ProfileAyaan Hossain, View ORCID ProfileHoward M. Salis
doi: https://doi.org/10.1101/2021.09.01.458561
Travis La Fleur
1Department of Chemical Engineering, Pennsylvania State University; University Park, Pennsylvania, 16801, United States
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Ayaan Hossain
2Bioinformatics and Genomics, Pennsylvania State University; University Park, Pennsylvania, 16801, United States
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Howard M. Salis
1Department of Chemical Engineering, Pennsylvania State University; University Park, Pennsylvania, 16801, United States
2Bioinformatics and Genomics, Pennsylvania State University; University Park, Pennsylvania, 16801, United States
3Department of Biological Engineering, Pennsylvania State University; University Park, Pennsylvania, 16801, United States
4Department of Biomedical Engineering, Pennsylvania State University; University Park, Pennsylvania, 16801, United States
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  • ORCID record for Howard M. Salis
  • For correspondence: salis@psu.edu
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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 σ70 promoter sequence, validated across 17396 bacterial promoters with diverse sequences. We applied 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.

One-Sentence Summary A 346-parameter model predicted DNA’s interactions with RNA polymerase initiation complex, enabling accurate transcription rate predictions and automated promoter design in bacterial genetic systems.

Competing Interest Statement

HMS is a founder of De Novo DNA. TL and AH declare no competing interests.

Footnotes

  • https://salislab.net/software/predict_promoter_calculator

  • https://salislab.net/software/design_promoter_calculator

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted September 01, 2021.
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Automated Model-Predictive Design of Synthetic Promoters to Control Transcriptional Profiles in Bacteria
Travis La Fleur, Ayaan Hossain, Howard M. Salis
bioRxiv 2021.09.01.458561; doi: https://doi.org/10.1101/2021.09.01.458561
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Automated Model-Predictive Design of Synthetic Promoters to Control Transcriptional Profiles in Bacteria
Travis La Fleur, Ayaan Hossain, Howard M. Salis
bioRxiv 2021.09.01.458561; doi: https://doi.org/10.1101/2021.09.01.458561

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