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

ABSTRACT: We report the construction of a novel Systems Biology based virtual drug discovery model for the prediction of non-toxic metabolic targets in Mycobacterium tuberculosis (Mtb). This is based on a data-intensive genome level analysis and the principle of conservation of the evolutionarily important genes. In the 1623 sequenced Mtb strains, 890 metabolic genes identified through a systems approach in Mtb were evaluated for 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 for their experimental and in silico essentiality as well as availability of their crystal structure in Protein Data Bank (PDB). Along with this, targets for the common existing antibiotics and the

SUBMITTER: Kaur D 

PROVIDER: S-EPMC5397868 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature