A Multi-Scale Computational Framework for Anti-Persister Tuberculosis Drug Discovery.

Latent tuberculosis (TB), caused by dormant, non-replicating Mycobacterium tuberculosis (Mtb), afflicts an estimated 1.7 billion individuals globally and remains a principal barrier to TB eradication. Dormant bacilli are phenotypically tolerant to conventional antibiotics and survive through continued dependence on oxidative phosphorylation (OXPHOS) and the electron transport chain (ETC), making bioenergetic enzymes, including the F1Fo-ATP synthase, cytochrome bcc:aa3 supercomplex, Type II NADH dehydrogenase (NDH-2) and cytochrome bd oxidase, compelling drug targets. This review proposes and rigorously analyses a synergistic, multi-scale computational workflow as a uniquely powerful paradigm for discovering anti-persister agents. The workflow cascades across four integrated stages: (1) dormancy-specific Quantitative Structure-Activity Relationship (QSAR) modelling using hypoxic assay data, with predictive models achieving R2 values of 0.83-0.98; (2) rational scaffold design and pharmacophore-guided hit expansion against structurally validated bioenergetic targets, incorporating bioisosteric replacement and fragment merging strategies; (3) structure-based molecular docking exploiting high-resolution cryo-EM structures (resolution ≤ 2.67 Å), with clinically validated inhibitors such as Bedaquiline (BDQ) yielding docking scores of -12.3 to -15.8 kcal/mol; and (4) advanced Molecular Dynamics (MD) simulation under physiologically relevant conditions, including Constant pH MD (CpHMD) at phagosomal pH 4.5 and all-atom mycomembrane models. A critically underexplored dimension, multi-target drug discovery, is systematically examined, focusing on the design of dual-pharmacophore inhibitors that simultaneously engage NDH-2 and cytochrome bd oxidase, thereby raising the genetic barrier to resistance. By framing discovery as a logical progression from statistical correlation (QSAR) through structural hypotheses (scaffold design and docking) to dynamic atomistic validation (MD), this review provides a rigorous, quantitatively grounded computational roadmap for the next generation of anti-persister therapeutics.