Structure-Guided Computational Approaches to Unravel Druggable Proteomic Landscape of Mycobacterium leprae - Vedithi, Sundeep Chaitanya; Malhotra, Sony; Acebrón-García-de-Eulate, Marta; Matusevicius, Modestas; Torres, Pedro Henrique Monteiro; Blundell, Tom L. (Frontiers in Molecular Biosciences, 2021)
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 favour 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. M. 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 essential including unravelling druggable targets that prevent its predilection for human peripheral neurons. Of the 1614 protein-coding genes in the genome of M. leprae, only 17 protein structures are available in the Protein Data Bank. In this review, we discuss efforts made to model the proteome of M. leprae using a suite of software for protein modelling that has been developed in the Blundell laboratory. Precise template selection by employing sequence-structure homology recognition software, multi-template modelling of the monomeric models and accurate quality assessment are the hallmarks of the modelling 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.