Identification of potential inhibitor molecule against MabA protein of Mycobacterium leprae by integrated in silico approach.

Leprosy is one of the chronic diseases with which humanity has struggled globally for millennia. The potent anti-leprosy medications rifampicin, clofazimine and dapsone, among others, are used to treat leprosy. Nevertheless, even in regions of the world where these drugs have been successfully implemented, resistance continues to be observed. Due to the problems with the current treatments, this disease should be fought at every level of society with new drugs. The purpose of this research was to identify natural candidates with the ability to inhibit MabA (gene-fabG1) with fewer negative effects. The work was accomplished through molecular docking, followed by a dynamic investigation of protein-ligand, which play a significant role in the design of pharmaceuticals. After modelling the protein structure with MODELLER 9.21v, AutoDock Vina was used to perform molecular docking with 13 3 D anti-leprosy medicines and a zinc library to determine the optimal protein-ligand interaction. In addition, the docking result was filtered based on binding energy, ADMET characteristics, PASS analysis and the most crucial binding residues. The ZINC08101051 chemical compound was prioritized for further study. Using an all-atom 100 ns MD simulation, the binding pattern and conformational changes in protein upon ligand binding were studied. Recommendation for subsequent validation based on deviation, fluctuation, gyration and hydrogen bond analysis, followed by main component and free energy landscape.Communicated by Ramaswamy H. Sarma.

Assessment of key regulatory genes and identification of possible drug targets for Leprosy (Hansen's disease) using network-based approach.

Leprosy is a major health concern and continues to be a source of fear and stigma among people worldwide. Despite remarkable achievements in the treatment, understanding of pathogenesis and transmission, epidemiology of leprosy still remains inadequate. The prolonged incubation period, slow rates of occurrence in those exposed and deceptive clinical presentation pose challenges to develop reliable strategies to stop transmission. Hence, there is a need for improved diagnostics and therapies to prevent mortality caused by leprosy. The objectives of this study are to identify significant genes from protein-protein interactions (PPIs) network of leprosy and to choose the most effective therapeutic targets. Fifty genes related with leprosy were discovered by literature mining. These genes were used to construct a primary network. Leading Eigen Vector method was used to break down the primary network into various sub-networks or communities. It was found that the primary network was divided into many sub-networks at the 6 levels. Seed genes were traced at each level till key regulatory genes were identified. Three seed genes, namely, GNAI3, NOTCH1, and HIF1A, were able to make their way till the final motif stage. These genes along with their interacting partners were considered key regulators of the leprosy network. This study provides leprosy-associated key genes which can lead to improved diagnosis and therapies for leprosy patients.