Predictive design of sigma factor-specific promoters

Maarten Van Brempt, Jim Clauwaert, Friederike Mey, Michiel Stock, Jo Maertens, Willem Waegeman and Marjan De Mey ()
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Maarten Van Brempt: Ghent University
Jim Clauwaert: Ghent University
Friederike Mey: Ghent University
Michiel Stock: Ghent University
Jo Maertens: Ghent University
Willem Waegeman: Ghent University
Marjan De Mey: Ghent University

Nature Communications, 2020, vol. 11, issue 1, 1-13

Abstract: Abstract To engineer synthetic gene circuits, molecular building blocks are developed which can modulate gene expression without interference, mutually or with the host’s cell machinery. As the complexity of gene circuits increases, automated design tools and tailored building blocks to ensure perfect tuning of all components in the network are required. Despite the efforts to develop prediction tools that allow forward engineering of promoter transcription initiation frequency (TIF), such a tool is still lacking. Here, we use promoter libraries of E. coli sigma factor 70 (σ70)- and B. subtilis σB-, σF- and σW-dependent promoters to construct prediction models, capable of both predicting promoter TIF and orthogonality of the σ-specific promoters. This is achieved by training a convolutional neural network with high-throughput DNA sequencing data from fluorescence-activated cell sorted promoter libraries. This model functions as the base of the online promoter design tool (ProD), providing tailored promoters for tailored genetic systems.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-19446-w

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DOI: 10.1038/s41467-020-19446-w

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