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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

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Snowprint: a predictive tool for genetic biosensor discovery.

Simon d'Oelsnitz^1,2, Sarah K Stofel³, Joshua D Love⁴

¹Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, 78712, USA. simonsnitz@gmail.com.

Communications Biology

|February 9, 2024

Summary

This summary is machine-generated.

A new bioinformatic tool, Snowprint, predicts gene regulator interactions, enabling the discovery of novel ligand-inducible transcription regulators for synthetic biology and chemical sensing applications.

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Area of Science:

Synthetic biology
Bioengineering
Genomics

Background:

Ligand-inducible transcription regulators are crucial for chemical control of gene expression in bioengineering.
Limited availability and poor understanding of DNA specificity of existing regulators hinder genetic design.
Mining novel regulators from genomes is challenging due to complex DNA interactions.

Purpose of the Study:

To develop a bioinformatic tool, Snowprint, for predicting regulator:operator interactions.
To facilitate the discovery and engineering of novel ligand-inducible transcription regulators.
To enable the creation of new biosensors for biomanufacturing applications.

Main Methods:

Development of Snowprint, a protein-agnostic bioinformatic tool for predicting regulator:operator DNA binding specificity.
Benchmarking Snowprint against experimentally validated regulator:operator pairs across diverse organisms and protein families.
Using Snowprint to design promoters for novel regulators and screening them for gene expression control and compound response.

Main Results:

Snowprint predictions showed significant similarity for over 45% of validated regulator:operator pairs across nine phyla and five structural families.
Promoters designed using Snowprint enabled gene expression control for 28 out of 33 previously uncharacterized regulators, with 24 showing >20-fold dynamic range.
Newly repurposed regulators were screened, leading to the discovery of sensors for a polyketide, terpene, steroid, and alkaloid with induction ratios up to 10.7-fold.

Conclusions:

Snowprint is a powerful and versatile tool for predicting regulator:operator interactions, significantly aiding the discovery of novel ligand-inducible transcription regulators.
The tool accelerates bioengineering by enabling the design of custom genetic circuits and biosensors.
Snowprint has practical applications in biomanufacturing, facilitating the development of sensors for valuable compounds.