Targeting Mycobacterial Dormancy Survival Regulator (DosR) With Generative Artificial Intelligence and Omics Methods

Tuberculosis, driven by Mycobacterium tuberculosis, remains a global health challenge due to the pathogen's ability to enter a dormant state, evading immune responses and conventional antibiotic treatments. The dormancy survival regulator (DosR) protein, a key transcriptional regulator, orchestrates this dormancy mechanism, making it an attractive target for therapeutic intervention. In this research, we applied a comprehensive in silico approach to identify potential inhibitors of DosR, combining domain and motif analysis, multiple sequence alignment (MSA), and consensus sequence generation to uncover conserved regions within the DosR protein across various Mycobacterium species. Initially, FDA-approved compounds were screened through molecular docking to identify candidates with promising binding affinities to the DosR active site. The top 100 compounds were then used for de novo molecule generation using REINVENT4, resulting in a new library of novel compounds. A rigorous absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis, molecular dynamics (MD) simulations, and MMGBSA led to top 5 selected compounds and confirmed their stability and strong interactions with the DosR protein. Key candidates, including RI081 (N-(4-(N-(cyclohexylcarbamoyl)sulfamoyl) benzyl)nicotinamide), RI089 ((S)-10-(((3-chlorophenyl)amino)methyl)-9-fluoro-3-methyl-7-oxo-2,3-dihydro-7H-[1,4]oxazino[2,3,4-ij]quinoline-6-carboxylic acid), and RI107 ((S)-2-((1r,4S)-4-methylcyclohexane-1-carboxamido)-3-(2-oxo-1,2-dihydroquinolin-4-yl)propanoic acid), emerged as the most promising inhibitors, demonstrating both stability and strong binding affinity. This multi-tiered approach, blending bioinformatics, molecular docking, and dynamics, presents a robust framework for discovery of DosR inhibitors.