Computational Biology in Anti-Tuberculosis Drug Discovery

Title: Computational Biology in Anti-Tuberculosis Drug Discovery

Volume: 9 Issue: 3

Author(s): Dennis J. Murphy and James R. Brown

Affiliation:

Keywords: Computational Biology, Drug Discovery, Anti-Tuberculosis, Mycobacterium tuberculosis (MTB), immune system, DNA sequences

Abstract: The resurgence of drug resistant tuberculosis (TB) is a major global healthcare problem. Mycobacterium tuberculosis (MTB), TBs 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.

Cite this article as:

Murphy J. Dennis and Brown R. James, Computational Biology in Anti-Tuberculosis Drug Discovery, Infectious Disorders - Drug Targets 2009; 9 (3) . https://dx.doi.org/10.2174/1871526510909030319