Computational identification of potential drug targets against Mycobacterium leprae

Leprosy is caused by Mycobacterium leprae a major health concern in several countries of the world particularly in Asia and Africa. The preventive measurement has been adopted by the combined efforts of the leprosy burden countries and WHO. However, the situation is getting worse due to the emergence of the resistant strains of the M. leprae. The continuous efforts are underway to discover new chemical agent as a therapeutic to cure the diseases caused by the resistant pathogens of bacterial origins. The resistant pathogens are still growing on alarming rate. In order to overcome the resistant pathogens, a relatively newer approach has been applied since last decade. One of them involves the computational subtractive genomics, in which the complete proteome of the bacterial pathogen is step-wise reduced to few potential drug targets. The steps include the finding of non-host proteins, essentiality of the proteins to the pathogens and involvement of the shortlisted proteins in essential metabolic pathways of the pathogen, which are necessary for the bacterial survival. In the current study, we applied computational subtractive genomics on complete proteome of the M. leprae and ended up with 16 hypothetical proteins as potential drug targets against which new active molecules can be proposed to ameliorate the activity to cure the disease associated with them. The study is innovative and has a potential to improve the research directions in unraveling the novel cure of leprosy.