Mycobacterium tuberculosis: Structural Biology, Clinical Management, and Computational Approaches to Combat Drug Resistance

Tuberculosis (TB), the highly virulent disease precipitated by Mycobacterium tuberculosis, continues to be a major infectious disease, with considerable morbidity and death rates worldwide. The organism’s complex biology, ability to endure in latent states, and evolving resistance to antibiotics pose major challenges to global health. Conventional treatment regimens, though effective in many cases, are increasingly undermined by the rise of multi- and extensively drug resistant TB strains. Combined with the slow-paced discovery of novel antibiotics generally, specifically anti-TB, novel methods for combatting the pathogen are urgently required. This mini review explores the organism’s structure, diagnostic and therapeutic strategies, the growing threat of antimicrobial resistance, and the novel methods for anti-TB drug discovery. Emphasis is placed on emerging computational approaches such as subtractive proteomic and machine learning that offer promising path for predicting drug efficacy, repurposing existent drugs and validating these predictions. Validation of these in silico methods through experimental data is essential to ensure their translational impact. Integrating computational tools with traditional TB management can speed up novel therapeutics’ development, eventually enhancing patient outcomes and aiding in global TB control efforts.