Computation-Guided Transcription Factor Biosensor Specificity Engineering for Adipic Acid Detection

Elsevier

Available online 15 May 2024

Computational and Structural Biotechnology JournalAuthor links open overlay panel, , , Highlights•

Established a computational workflow for ligand specificity engineering

Computation-assisted protein engineering of the binding pocket of BenM

Ligand docking can identify hotspot residues for ligand specificity

Cell-free systems can improve sensitivity

Molecular dynamics can be used to analyze the impact of mutations on TFs

Abstract

Transcription factor (TF)-based biosensors that connect small-molecule sensing with readouts such as fluorescence have proven to be useful synthetic biology tools for applications in biotechnology. However, the development of specific TF-based biosensors is hindered by the limited repertoire of TFs specific for molecules of interest since current construction methods rely on a limited set of characterized TFs. In this study, we present an approach for engineering the specificity of TFs through a computation-based workflow using molecular docking that enables targeted alteration of TF ligand specificity. Using this method, we engineer the LysR family BenM TF to alter its specificity from its cognate ligand cis,cis-muconic acid to adipic acid through a single amino acid substitution identified by our computational workflow. When implemented in a cell-free system, the engineered biosensor shows higher ligand sensitivity, expanding the potential applications of this circuit. We further investigate ligand binding through molecular dynamics to analyze the substitution, elucidating the impact of modulating a single amino acid position on the mechanism of BenM ligand binding. This study represents the first application of biomolecular modeling methods for altering BenM specificity and for gaining insights into how mutations influence the structural dynamics of BenM. Such methods can potentially be applied to other TFs to alter specificity and analyze the dynamics responsible for these changes, highlighting the applicability of computational tools for informing experiments. In addition, our developed adipic acid biosensor can be applied for the identification and engineering of enzymes to produce adipic acid.

Keywords

biosensors

muconic acid

adipic acid

docking

protein engineering

molecular dynamics

© 2024 Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.

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