Identification of potential inhibitors against E.coli via novel approaches based on deep learning and quantum mechanics-based atomistic investigations.

ABSTRACT

Currently, drug resistance to commercially available antibiotics is imparting negative consequences to global health, and the development of novel antibiotics in a timely manner is a prime need of the hour. In the current study, an e-pharmacophore model was built using the 3D structure of DNA gyrase in complex with a standard inhibitor. The generated model was subjected to a pharmacophore based virtual screening against 45,257,086 molecules having 223,460,579 conformers available in MCULE database. Pharmacophore based screening retrieved eight molecules as top hit based on pharmacophoric features in comparison to standard inhibitors. Afterward, all eight compounds were subjected molecular docking based on deep learning algorithm. The molecular docking revealed that compound MCULE-6042843173 and MCULE-2362244223 had significant binding orientation inside active pocket of targeted protein with binding affinity of -9.52 and -9.24 kcal/mol respectively. In addition, density functional theory studies (DFT) were performed to evaluate quantum mechanics of top ranked compounds which were investigated through quantum mechanics (QM) computations which strongly assisted the findings of other in-silico investigations. Consequently, the MCULE-6042843173 and MCULE-2362244223 were subjected to MD simulation studies for evaluation of stability, hydrogen bond analysis, van der Waals interactions, and the contact profile of compounds with targeted amino acid residues. Findings of current study suggested MCULE-6042843173 and MCULE-2362244223 as potential and novel inhibitor of DNA Gyrase enzyme.