Title:	Computational studies on bacterial transcriptional repressor NrdR
DNr:	NAISS 2025/5-301
Project Type:	NAISS Medium Compute
Principal Investigator:	Derek Logan <derek.logan@biochemistry.lu.se>
Affiliation:	Lunds universitet
Duration:	2025-05-30 – 2026-06-01
Classification:	10601 10407 30103
Keywords:

Abstract

Antimicrobial resistance represents a critical global public health challenge, with the ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa) and Escherichia coli posing a significant worldwide threat. These pathogens exhibit remarkable adaptability to healthcare environments and possess multidrug-resistant mechanisms, presenting substantial obstacles in the development of novel therapeutic interventions. Ribonucleotide reductases (RNRs) are essential enzymes that catalyze the synthesis of deoxyribonucleotides required for DNA replication and repair. In bacteria, the transcriptional repressor NrdR acts as a universal suppressor of all RNR classes, effectively regulating nucleotide synthesis and homeostasis. Current challenges in targeting bacterial RNRs lie in their redundancy. Blocking one RNR class can upregulate others, allowing bacterial survival. However, stimulating the DNA binding activity of NrdR halts the transcription of all bacterial RNRs simultaneously without affecting human counterparts, presenting a unique and innovative mechanism to overcome resistance. High-resolution crystal structures of NrdR from E. coli and S. coelicolor provides structural insights into its DNA-bound and inactive conformations. Given NrdR's ubiquity across bacterial genomes, this target holds the potential for broad-spectrum antibacterial therapies, including multidrug-resistant strains with no current treatment options. Modern techniques, such as Computer Aided Drug Design (CADD), have been incorporated into the drug discovery pipeline, and by using these computational approaches researchers can accelerate the identification of promising drug candidates, optimize their properties, reduce development time, costs and its impact of residual chemicals in the environment. Given the above, this proposal aims to conduct Virtual Screening campaigns to filter millions of compounds and identify potential candidates for experimental validation. Our approach integrates similarity search, molecular docking and experimental assays. This strategy is designed to outcome promising candidates, paving the way for the development of innovative antibiotics targeting NrdR.