Data Intensive Genome Level Analysis from Identifying Novel, Non-Toxic Drug Targets from Multi Drug Resistant Mycobacterium tuberculosis

Abstract

We report the construction of a novel Systems Biology based virtual drug discovery model from the prediction of non-toxic metabolic targets in Mycobacterium tuberculosis (Mtb). This is based on a dataintensive genome level analysis and the principle of conservation of the evolutionarily important genes. In the 1623 sequenced Mtb strains, 890 metabolic genes identiifed through a systems approach in Mtb were evaluated from non-synonymous mutations. The 33 genes showed none or one variation in the entire 1623 strains, including 1084 Russian MDR strains. These invariant targets were further evaluated from their experimental and in silico essentiality as well as availability of their crystal structure in Protein Data Bank (PDB). Along with this, targets from the common existing antibiotics and the new Tb drug candidates were also screened from their variation across 1623 strains of Mtb from understanding the drug resistance. We propose that the reduced set of these reported targets could be a more effective starting point from medicinal chemists in generating new chemical leads. This approach has the potential of fueling the dried up Tuberculosis (Tb) drug discovery pipeline.