Ligify: Automated Genome Mining for Ligand-Inducible Transcription Factors

Area of Science:

• Synthetic biology
• Molecular biology
• Bioengineering

Background:

• Prokaryotic transcription factors are limited in their use as biosensors for chemical-inducible gene expression.
• A broader range of chemical ligands is needed for effective biosensor development.

Purpose of the Study:

• To develop Ligify, a web application for predicting transcription factors responsive to user-defined chemicals.
• To automate the design of plasmid sequences for biosensor construction.
• To expand the landscape of chemical ligands controllable by biosensors.

Main Methods:

• Leveraging enzyme reaction databases to predict transcription factor-ligand interactions.
• Automated generation of plasmid sequences for biosensor expression.
• Benchmarking Ligify predictions against known biosensors.
• Experimental validation of newly designed biosensors and genetic circuits.

Main Results:

• Ligify successfully predicted 31 out of 100 previously validated biosensors.
• Developed genetic circuits showing significant fluorescence changes (47-fold, 5-fold, 9-fold, 27-fold) for D-ribose, L-sorbose, isoeugenol, and 4-vinylphenol.
• Identified areas for improvement in biosensor prediction and design.

Conclusions:

• Ligify enhances the rapid development of biosensors for a wider array of chemicals.
• The tool facilitates the creation of novel ligand-inducible gene expression systems.
• Ligify is publicly available to aid researchers in synthetic biology applications.