TB Research

A FRET-based high-throughput screening assay for the discovery of <i>Mycobacterium tuberculosis</i> DNA ADP-ribosylglycohydrolase DarG inhibitors

Men Thi Hoai Duong, Lu Yang, Ivan Ahel, L. Lehtiö

bioRxiv (Cold Spring Harbor Laboratory) · 2025-07

Abstract

DarTG2 is a conserved toxin-antitoxin ADP-ribosylation system that regulates bacterial survival and the anti-phage response found in many pathogenic bacteria, including Mycobacterium tuberculosis . While DNA ADP-ribosyltransferase (DarT2) toxin mono-ADP-ribosylates a single-stranded DNA sequence motif and potentially induces bacterial dormancy, its cognate DNA ADP-ribosylglycohydrolase (DarG) antitoxin reverses the modification and restores bacterial growth. Therefore, developing DarG selective inhibitors may represent a promising and novel strategy to combat drug-resistant tuberculosis. However, no small molecule inhibitors targeting DarG have been identified to date and establishing a high-throughput screening assay based on its DNA ADP-ribosylhydrolase activity would be challenging. Here, we developed and optimized a simple and robust fluorescence resonance energy transfer (FRET)-based high-throughput screening assay to identify small molecule inhibitors targeting DarG macrodomain in M. tuberculosis . We generated a FRET pair of M. tuberculosis DarG macrodomain and poly-ADP-ribosylated peptide fused with compatible fluorophores. Screening the target-focused phenotypic library using this method led to the identification of pranlukast, which selectively inhibited the DNA ADP-ribosylhydrolase activity of DarG in M. tuberculosis and its orthologues without affecting human mono-ADP-ribosyl binders and erasers. Since pranlukast has previously been reported to reduce M. tuberculosis burden, further investigation into its action mechanism in this context would be valuable.

MeSH terms

  • Mycobacterium tuberculosis
  • High-throughput screening
  • Tuberculosis
  • Förster resonance energy transfer
  • Drug discovery
  • DNA
  • Virology
  • Chemistry
  • Computational biology