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Title

 

 

 

 

 

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

Authors

 

Anusuya Shanmugam1, Jeyakumar Natarajan2*

Affiliation

 

1Department of Bioinformatics, VMKV Engineering College, Vinayaka Missions University, Salem; 2Department of Bioinformatics, Bharathiar university, Coimbatore

 

Email

 

n.jeyakumar@yahoo.co.in

 

Article Type

 

Hypothesis

Date

 

Received November 04, 2009; accepted March 06, 2010; published March 31, 2010

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.

 

Keywords

Comparative genomics, Mur enzymes, M. leprae, Leprosy.

 

Citation

 

Shanmugam & Natarajan, Bioinformation 4(9): 392-395 (2010)

 

Edited by

 

P. Kangueane

ISSN

 

0973-2063

 

Publisher

 

Biomedical Informatics

License

 

 

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.