Computational biology in anti-tuberculosis drug discovery

The resurgence of drug resistant tuberculosis (TB) is a major global healthcare problem. Mycobacterium tuberculosis (MTB), TB's causative agent, evades the host immune system and drug regimes by entering prolonged periods of nonproliferation or dormancy. The identification of genes essential to the bacterium in its dormancy phase infections is a key strategy in the development of new anti-TB therapeutics. The rapid expansion of TB-related genomic data sources including DNA sequences, transcriptomic and proteomic profiles, and genome-wide essentiality data, present considerable opportunities to apply advanced computational analyses to predict potential drug targets. However, the translation of in silico predictions to effective clinical therapies remains a significant challenge.