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Rapid Synthesis and Screening of Chemically Activated Transcription Factors with GFP-based Reporters

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Published on: November 26, 2013

Computation-guided transcription factor biosensor specificity engineering for adipic acid detection.

Chester Pham¹, Peter J Stogios¹, Alexei Savchenko^1,2

¹Department of Chemical Engineering and Applied Chemistry, University of Toronto, Ontario, Canada.

Computational and Structural Biotechnology Journal

|May 31, 2024

Summary

This summary is machine-generated.

We engineered transcription factor (TF)-based biosensors for biotechnology by computationally altering TF specificity. This approach enables the creation of novel biosensors, like one for adipic acid, with enhanced sensitivity.

Keywords:

Adipic acid Biosensors Docking Molecular dynamics Muconic acid Protein engineering

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Area of Science:

Synthetic Biology
Biotechnology
Computational Biology

Background:

Transcription factor (TF)-based biosensors are valuable tools in biotechnology, linking small-molecule detection to measurable outputs like fluorescence.
Current TF biosensor development is limited by the scarcity of TFs specific to desired molecules, restricting the construction of new biosensors.

Purpose of the Study:

To present a computational workflow for engineering TF ligand specificity.
To demonstrate the targeted alteration of TF specificity using molecular docking.
To develop a novel biosensor for adipic acid production.

Main Methods:

Employed a computation-based workflow utilizing molecular docking to identify targeted amino acid substitutions for altering TF specificity.
Engineered the LysR family TF, BenM, to switch specificity from cis,cis-muconic acid to adipic acid via a single amino acid change.
Utilized molecular dynamics simulations to analyze ligand binding and the impact of the amino acid substitution on BenM's structural dynamics.

Main Results:

Successfully engineered the BenM TF to specifically bind adipic acid through a single amino acid substitution.
The engineered adipic acid biosensor exhibited enhanced ligand sensitivity in a cell-free system.
Molecular dynamics revealed how the single amino acid change influences BenM's ligand binding mechanism and structural dynamics.

Conclusions:

This study demonstrates the first application of biomolecular modeling to alter BenM specificity and understand mutation-induced dynamic changes.
The computational approach offers a powerful strategy for engineering specificity in other TFs and analyzing their dynamic mechanisms.
The developed adipic acid biosensor has potential applications in identifying and engineering enzymes for adipic acid production.