Dataset Information

Computational genome analyses of metabolic enzymes in Mycobacterium leprae for drug target identification.

ABSTRACT: Leprosy is an infectious disease caused by Mycobacterium leprae. M. leprae has undergone a major reductive evolution leaving a minimal set of functional genes for survival. It remains non-cultivable. As M. leprae develops resistance against most of the drugs, novel drug targets are required in order to design new drugs. As most of the essential genes mediate several biosynthetic and metabolic pathways, the pathway predictions can predict essential genes. We used comparative genome analysis of metabolic enzymes in M. leprae and H. sapiens using KEGG pathway database and identified 179 non-homologues enzymes. On further comparison of these 179 non-homologous enzymes to the list of minimal set of 48 essential genes required for cell-wall biosynthesis of M. leprae reveals eight common enzymes. Interestingly, six of these eight common enzymes map to that of peptidoglycan biosynthesis and they all belong to Mur enzymes. The machinery for peptidoglycan biosynthesis is a rich source of crucial targets for antibacterial chemotherapy and thus targeting these enzymes is a step towards facilitating the search for new antibiotics.

SUBMITTER: Shanmugam A

PROVIDER: S-EPMC2951640 | biostudies-literature | 2010 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Computational genome analyses of metabolic enzymes in Mycobacterium leprae for drug target identification.

Shanmugam Anusuya A Natarajan Jeyakumar J

Bioinformation 20100331 9

Leprosy is an infectious disease caused by Mycobacterium leprae. M. leprae has undergone a major reductive evolution leaving a minimal set of functional genes for survival. It remains non-cultivable. As M. leprae develops resistance against most of the drugs, novel drug targets are required in order to design new drugs. As most of the essential genes mediate several biosynthetic and metabolic pathways, the pathway predictions can predict essential genes. We used comparative genome analysis of me ...[more]

PMID: 20975887

Similar Datasets

Project description:Leprosy, caused by Mycobacterium leprae (M. leprae), is treated with a multidrug regimen comprising Dapsone, Rifampicin, and Clofazimine. These drugs exhibit bacteriostatic, bactericidal and anti-inflammatory properties, respectively, and control the dissemination of infection in the host. However, the current treatment is not cost-effective, does not favor patient compliance due to its long duration (12 months) and does not protect against the incumbent nerve damage, which is a severe leprosy complication. The chronic infectious peripheral neuropathy associated with the disease is primarily due to the bacterial components infiltrating the Schwann cells that protect neuronal axons, thereby inducing a demyelinating phenotype. There is a need to discover novel/repurposed drugs that can act as short duration and effective alternatives to the existing treatment regimens, preventing nerve damage and consequent disability associated with the disease. Mycobacterium leprae is an obligate pathogen resulting in experimental intractability to cultivate the bacillus in vitro and limiting drug discovery efforts to repositioning screens in mouse footpad models. The dearth of knowledge related to structural proteomics of M. leprae, coupled with emerging antimicrobial resistance to all the three drugs in the multidrug therapy, poses a need for concerted novel drug discovery efforts. A comprehensive understanding of the proteomic landscape of M. leprae is indispensable to unravel druggable targets that are essential for bacterial survival and predilection of human neuronal Schwann cells. Of the 1,614 protein-coding genes in the genome of M. leprae, only 17 protein structures are available in the Protein Data Bank. In this review, we discussed efforts made to model the proteome of M. leprae using a suite of software for protein modeling that has been developed in the Blundell laboratory. Precise template selection by employing sequence-structure homology recognition software, multi-template modeling of the monomeric models and accurate quality assessment are the hallmarks of the modeling process. Tools that map interfaces and enable building of homo-oligomers are discussed in the context of interface stability. Other software is described to determine the druggable proteome by using information related to the chokepoint analysis of the metabolic pathways, gene essentiality, homology to human proteins, functional sites, druggable pockets and fragment hotspot maps.

Dataset Information

Computational genome analyses of metabolic enzymes in Mycobacterium leprae for drug target identification.

Publications

Computational genome analyses of metabolic enzymes in Mycobacterium leprae for drug target identification.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets