Identifying dormancy-associated enzymes in Mycobacterium tuberculosis through a computational pipeline integrating flux balance analysis and metabolic modeling.

Tuberculosis, caused by Mycobacterium tuberculosis (Mtb), remains a critical global health challenge due to rising drug resistance and the pathogen's ability to persist in hostile host environments. Identifying novel molecular targets that underlie Mtb's unique survival mechanisms is essential for developing more effective therapies. In this study, we developed an integrative computational pipeline combining genome-scale metabolic modeling, flux balance analysis (FBA), comparative genomics, and network-based prioritization to uncover metabolic vulnerabilities specific to Mtb. Comparative analysis with the reductively evolved Mycobacterium leprae revealed significant differences in pathways involved in pantothenate biosynthesis (PanB), peptidoglycan synthesis (GlmU), and branched-chain amino acid metabolism (IlvN). These targets were prioritized based on gene essentiality, dormancy-associated expression, druggability, and absence of human homologs to maximize therapeutic selectivity. Molecular docking, followed by MM-GBSA binding free energy calculations, identified high-affinity ligands from LifeChemicals and ChEMBL libraries interacting strongly with active-site residues. Molecular dynamics simulations were performed to further validate target engagement and ligand retention, revealing stable conformational behavior and persistent protein-ligand interactions across 300 ns. Similarly, metabolite flux analysis and pathway enrichment highlighted adaptive rewiring in glycine, serine, pyruvate, and nitrogen metabolism, reflecting Mtb's persistence strategies under host-imposed stress. This study provides a robust, generalizable pipeline for pathogen-specific drug target and ligand discovery and supports the rational development of new therapies against drug-resistant tuberculosis.