Harnessing Bioinformatics to Decode Drug Resistance and Guide Vaccine Design in Mycobacterium tuberculosis

Abstract

Mycobacterium tuberculosis presents a major challenge to global health due to tuberculosis (TB), which is worsened by the emergence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) strains. This study provides a thorough examination of bioinformatics research, highlighting how the integration of genomic, proteomic and metabolomic data into TB research has revolutionised the way TB is studied, particularly in understanding resistance mechanisms, identifying new therapeutic targets, and expediting the development of diagnostic tools and vaccines. Whole-genome sequencing (WGS) and newer technologies like Oxford Nanopore Technologies (ONT) have since paved the way to the rapid identification of strain-specific mutations, with novel bioinformatics tools like PhyResSE, Mykrobe and GBOOST offering a powerful platform in mutation profiling, resistance prediction and strain classification. Furthermore, resistance prediction is also being transformed by machine learning and AI-based models, which improve the accuracy of clinical choices and monitoring options. Epitope prediction, molecular docking, and analysis of population coverage to inform the selection of promising immunogenic candidates are also proposed in the study as an in silico pipeline of rational vaccine design. Although continued efforts at infrastructure, standardisation, and workforce training are always ongoing in many settings where this is still seen as a major issue, especially in high-burden settings, this paradigm shift in an interdisciplinary approach is a clear indication of progress in the fight against one of the most prevalent and persistent infectious diseases in the world.