Predictive design of sigma factor-specific promoters

Nature Communications
Maarten Van BremptMarjan De Mey

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.

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Citations

May 1, 2021·International Journal of Molecular Sciences·Rubén de DiosFrancisca Reyes-Ramírez
May 8, 2021·Microbial Biotechnology·Xinyi WanBaojun Wang
Jun 29, 2021·Frontiers in Molecular Biosciences·Jan ZrimecAleksej Zelezniak

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Methods Mentioned

BETA
fluorescence-activated cell sorting
FACS
genotyping
flow cytometry
PCR
Assay
feature extraction

Software Mentioned

Tecan i - control
pLibrary
R
RNAcofold
Promoter Designer
WebLogo3
ProD
Magellan
ViennaRNA
PyTorch

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