alexa Potential drug targets in Mycobacterium tuberculosis through metabolic pathway analysis.
Biochemistry

Biochemistry

Biochemistry & Analytical Biochemistry

Author(s): Anishetty S, Pulimi M, Pennathur G

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Abstract The emergence of multidrug resistant varieties of Mycobacterium tuberculosis has led to a search for novel drug targets. We have performed an insilico comparative analysis of metabolic pathways of the host Homo sapiens and the pathogen M. tuberculosis. Enzymes from the biochemical pathways of M. tuberculosis from the KEGG metabolic pathway database were compared with proteins from the host H. sapiens, by performing a BLASTp search against the non-redundant database restricted to the H. sapiens subset. The e-value threshold cutoff was set to 0.005. Enzymes, which do not show similarity to any of the host proteins, below this threshold, were filtered out as potential drug targets. We have identified six pathways unique to the pathogen M. tuberculosis when compared to the host H. sapiens. Potential drug targets from these pathways could be useful for the discovery of broad spectrum drugs. Potential drug targets were also identified from pathways related to lipid metabolism, carbohydrate metabolism, amino acid metabolism, energy metabolism, vitamin and cofactor biosynthetic pathways and nucleotide metabolism. Of the 185 distinct targets identified from these pathways, many are in various stages of progress at the TB Structural Genomics Consortium. However, 67 of our targets are new and can be considered for rational drug design. As a case study, we have built a homology model of one of the potential drug targets MurD ligase using WHAT IF software. The model could be further explored for insilico docking studies with suitable inhibitors. The study was successful in listing out potential drug targets from the M. tuberculosis proteome involved in vital aspects of the pathogen's metabolism, persistence, virulence and cell wall biosynthesis. This systematic evaluation of metabolic pathways of host and pathogen through reliable and conventional bioinformatic methods can be extended to other pathogens of clinical interest. This article was published in Comput Biol Chem and referenced in Biochemistry & Analytical Biochemistry

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