Docking-based virtual screening of known drugs against murE of Mycobacterium tuberculosis towards repurposing for TB

Abstract

Repurposing has gained momentum globally and become an alternative avenue for drug discovery because of its better success rate, and reduced cost, time and issues related to safety than the conventional drug discovery process. Several drugs have already been successfully repurposed for other clinical conditions including drug resistant tuberculosis (DR-TB). Though TB can be cured completely with the use of currently available anti-tubercular drugs, emergence of drug resistant strains of Mycobacterium tuberculosis and the huge death toll globally, together necessitate urgently newer and effective drugs for TB. Therefore, we performed virtual screening of 1554 FDA approved drugs against murE, which is essential for peptidoglycan biosynthesis of M. tuberculosis. We used Glide and AutoDock Vina for virtual screening and applied rigid docking algorithm followed by induced fit docking algorithm in order to enhance the quality of the docking prediction and to prioritize drugs for repurposing. We found 17 drugs binding strongly with murE and three of them, namely, lymecycline, acarbose and desmopressin were consistently present within top 10 ranks by both Glide and AutoDock Vina in the induced fit docking algorithm, which strongly indicates that these three drugs are potential candidates for further studies towards repurposing for TB.

DockingApp RF: A State-of-the-Art Novel Scoring Function for Molecular Docking in a User-Friendly Interface to AutoDock Vina

“…Due to the relevance and success achieved by drug repurposing initiatives [ 49 , 50 , 51 ], the ready-to-be-docked library of the original DockingApp is now expanded in DockingApp RF. Indeed, the original collection of about 1400 FDA-approved drugs [ 52 ] is integrated with 4288 drugs approved by major national regulatory agencies and includes tautomers and different protonation states.…”

Drug targets for COVID-19 therapeutics: Ongoing global efforts

“…Here, by using structure-based computational strategy, binding efficiency is predicted between the drug and the target molecule (Elfiky 2020). With this method large-and small-scale screens can be conducted with known drugs against a disease target (Brindha et al 2016;Kitchen et al 2004). However, this technique has its own limitations, such as, for many targets 3D structure is not elucidated or there is a lack of available screen-able macromolecular database that can provide structural information for a varied molecular class of drug (Xia 2017;Zhou et al 2019).…”

In Silico Strategies in Tuberculosis Drug Discovery

“…Structural ensembles provide better reproducibility of experimental conditions as rigid protein structures, such as those obtained from X-ray crystallography experiments, only provide a snapshot of a dynamic ensemble of conformations. Various docking approaches were applied by Brindha et al [199] for drug repurposing against Mtb murE, which is an attractive target due to its significance in the peptidoglycan biosynthesis of tuberculosis bacteria and lack of eukaryotic homolog. VS of compounds from DrugBank [200] was first implemented through the parallel use of Glide Standard Precision (SP) [66] and AutoDock Vina [63].…”

“…To improve prioritization of compounds through the incorporation of binding site flexibility, common hits from both methods were further subjected to IFD [67]. Final rankings were done using Glide eXtra Precision (XP) scoring and AutoDock Vina binding energy prediction, resulting in 17 common top hits identified as repurposed antitubercular drug candidates [199]. In another example, ensemble docking using three enzyme structures was performed to better elucidate ligand binding interactions, especially due to the binding site flexibility of the Mtb Type II dehydroquinase (MtDHQase), an essential virulence factor in TB [201].…”