In Silico Activity Prediction and Docking Studies of the Binding Mechanisms of Levofloxacin Structure Derivatives to Active Receptor Sites of Bacterial Type IIA Topoisomerases

Abstract

The need for new antimicrobial agents (AntAg) is driven by the persistent antibiotic resistance in microorganisms, as well as the increasing frequency of pandemics. Due to the deficiency of AntAg, research aimed at developing speedy approaches to find new drug candidates is relevant. This study aims to conduct an in silico study of the biological activity spectrum as well as the molecular binding mechanisms of four structurally different forms of levofloxacin (Lvf) with bacterial topoisomerases targets of type IIA (DNA gyrase and topoisomerase IV) to enable the development of drugs with an improved characterization of the safety profile. To achieve this goal, a number of software products were used, such as ChemicPen v. 2.6, PyMol 2.5, Avogadro 1.2.0, PASS, AutoDockTools 1.5.7 with the new generation software Autodock Vina. These software products are the first to be made available for visualization of clusters with determination of ligand-receptor pair binding affinity, as well as clustering coordinates and proposed mechanisms of action. One of the real structures of Lvf, a decarboxylated derivative, was obtained with tribochemical (TrbCh) exposure. The action spectrum of molecular ligands is described based on a Bayesian probability activity prediction model (PASS software Version 2.0). Predicted and real (PMS and RMS) molecular structures of Lvf, with decreasing levels of structural complexity, were translated into descriptors via Wiener (W), Balaban (Vs), Detour (Ip), and Electropy € indices. The 2D «structure-activity» diagrams were used to differentiate closely related structures of levofloxacin. PMS and RMS were visualized as 3D models of the ligand-receptor complexes. The contact regions of RMS and PMS with key amino acid residues—SER-79, DT-15, DG-1, DA-1—were demonstrated. The intra- and inter-molecular binding sites, data on free energy (affinity values, kcal/mol), the binding constant Kb (M−1), and the number of clusters are presented. The research results obtained from the presented in silico approach to explore the spectrum of action find quantitative “structure-activity” correlations, and predict molecular mechanisms may be of applied interest for directed drug discovery.