Ligify: Automated Genome Mining for Ligand-Inducible Transcription Factors

Abstract

Prokaryotic transcription factors can be repurposed into biosensors for the ligand-inducible control of gene expression, but the landscape of chemical ligands for which biosensors exist is extremely limited. To expand this landscape, we developed Ligify, a web application that leverages information in enzyme reaction databases to predict transcription factors that may be responsive to user-defined chemicals. Candidate transcription factors are then incorporated into automatically generated plasmid sequences that are designed to express GFP in response to the target chemical. Our benchmarking analyses demonstrated that Ligify correctly predicted 31/100 previously validated biosensors and highlighted strategies for further improvement. We then used Ligify to build a panel of genetic circuits that could induce a 47-fold, 5-fold, 9-fold, and 27-fold change in fluorescence in response to D-ribose, L-sorbose, isoeugenol, and 4-vinylphenol, respectively. Ligify should enhance the ability of researchers to quickly develop biosensors for an expanded range of chemicals and is publicly available at https://ligify.groov.bio.

Keywords: bioinformatics; biosensor; genome mining; ligand; transcription factor; web application.

Figure 1

Ligify workflow. An input chemical name, SMILES code, or structure is used to fetch reactions associated with that chemical in the Rhea database. (1) Reactions associated with the input chemical are fetched from the Rhea database. (2) Phylogenetically diverse microbial enzymes associated with each reaction are returned. (3) Local genetic context of each enzyme is screened for regulators and if a regulator is identified, (4) a plasmid is designed, wherein the regulator is expressed from a constitutive promoter, and the GFP gene is expressed from the interoperon region assumed to contain a regulated promoter. (5) Predicted regulators and associated metrics are displayed in a web browser.

Figure 2

Benchmarking Ligify. (a) Benchmarking workflow. Ligands from a data set of experimentally validated ligand–regulator pairs are passed into Ligify, and the predicted regulators are then compared to the sequence of the validated regulator. (b) Accuracy of Ligify and TFBMiner. (c) Comparison of the regulators correctly predicted by Ligify and TFBMiner. (d) Analysis of the failure modes encountered during benchmarking of Ligify and TFBMiner. An in depth analysis of failure modes, as well as all associated benchmarking metrics, can be found in Supplementary Data 1.

Figure 3

Validation of autogenerated reporter plasmids. (a) Fluorescent response of E. coli bearing Ligify-generated plasmid designs when induced with predicted effector molecules. The ligand concentration for induction was 5 mM for D-ribose and L-sorbose and 1 mM for all other ligands, which was chosen based on the compound’s solubility limit in 1% DMSO. Error bars represent the standard deviation from the mean. (b–e) Dose response measurements of the LcLacI, sorR, vprR, and iemR regulators to their cognate ligands, respectively. Assays were performed in biological triplicate, and individual data points are shown. kMEF units represent molecules of equivalent fluorescein from the calibration of cytometry data with fluorescent beads.

Figure 4

Characterization of biosensor selectivity. (a–d) Dose response of Ligify-generated plasmids to the cognate ligand and ligand analogs for the LcLacI, vprR, sorR, and iemR regulators, respectively. Assays were performed in at least biological triplicate, and individual data points are shown. The maximum ligand concentration was chosen based on the compound’s solubility limit in 1% DMSO. kMEF units represent molecules of equivalent fluorescein from the calibration of cytometry data with fluorescent beads. Associated performance metrics are summarized in Table 1.