Implementation of computational strategies to combat drug resistance in Mycobacterium tuberculosis

Mycobacterium tuberculosis (MTB) is an infectious pathogen that causes tuberculosis (TB). Even though it is curable, the first-line anti-TB medication rifampicin has been a source of rising concern for human health across the globe. However, rifampicin resistance results from mutations in the RNA polymerase's binding site of the target protein. This infectious pathogen can survive in macrophages and can induce a long-lasting latent infection. The prolonged survival of mycobacterial species was achieved with the aid of serine/threonine protein kinase G (PknG) which performs a preventive role in this situation. Thus, PknG is believed to be an essential target to prevent the pathogen from entering the latency stage. The present investigation was oriented towards molecular-based virtual screening carried out using AutoDock for the FDA-approved and nutraceutical subsets of compounds from the DrugBank repository. Initially, a total of 3035 compounds were screened based on their absorption, distribution, metabolism, excretion and toxicity (ADMET) properties. The screened molecules were taken for docking purposes after which post-docking analysis was performed by calculating the random forest (RF) score. Further, the structural characterization of hit compounds was validated using interaction studies and in silico bioactivity prediction studies. Finally, the anti-myotubular activity prediction was carried out in the search for potential inhibitors. Importantly, four potential ligands, namely DB00080, DB00807, DB06249 and DB06274 were preferred after a thorough investigation. These ligands showed significant structural and protein-ligand interactions, suggesting that they might be potentially used as therapeutic candidates to combat MTB.