In silico characterization of aryl benzoyl hydrazide derivatives as potential inhibitors of RdRp enzyme of H5N1 influenza virus

Abstract

RNA-dependent RNA polymerase (RdRp) is a potential therapeutic target for the discovery of novel antiviral agents for the treatment of life-threatening infections caused by newly emerged strains of the influenza virus. Being one of the most conserved enzymes among RNA viruses, RdRp and its inhibitors require further investigations to design novel antiviral agents. In this work, we systematically investigated the structural requirements for antiviral properties of some recently reported aryl benzoyl hydrazide derivatives through a range of in silico tools such as 2D-quantitative structure-activity relationship (2D-QSAR), 3D-QSAR, structure-based pharmacophore modeling, molecular docking and molecular dynamics simulations. The 2D-QSAR models developed in the current work achieved high statistical reliability and simultaneously afforded in-depth mechanistic interpretability towards structural requirements. The structure-based pharmacophore model developed with the docked conformation of one of the most potent compounds with the RdRp protein of H5N1 influenza strain was utilized for developing a 3D-QSAR model with satisfactory statistical quality validating both the docking and the pharmacophore modeling methodologies performed in this work. However, it is the atom-based alignment of the compounds that afforded the most statistically reliable 3D-QSAR model, the results of which provided mechanistic interpretations consistent with the 2D-QSAR results. Additionally, molecular dynamics simulations performed with the apoprotein as well as the docked complex of RdRp revealed the dynamic stability of the ligand at the proposed binding site of the receptor. At the same time, it also supported the mechanistic interpretations drawn from 2D-, 3D-QSAR and pharmacophore modeling. The present study, performed mostly with open-source tools and webservers, returns important guidelines for research aimed at the future design and development of novel anti-viral agents against various RNA viruses like influenza virus, human immunodeficiency virus-1, hepatitis C virus, corona virus, and so forth.

Shaping the Future of Obesity Treatment: In Silico Multi-Modeling of IP6K1 Inhibitors for Obesity and Metabolic Dysfunction

“…The docked complexes underwent extensive 50 ns MD simulations, following well-established procedures that have been described in detail elsewhere [ 58 , 59 ]. Briefly, the ligand parameterizations were conducted using Leap, implemented in Amber 14, employing the general AMBER force field (GAFF) in Antechamber.…”

“…Normal mode analyses, which are based on a quasi-harmonic entropy approach, involve calculating the covariance matrix of the atomic displacements from their average positions across the sampled trajectory. Additionally, we assessed the energy contributions of the binding site amino acid residues using the per-residue free energy decomposition method and the Amber MM-GBSA module [ 58 , 62 ]. All the energy components, including the van der Waals, electrostatic, polar solvation, and non-polar solvation contributions, were calculated using 200 snapshots extracted from the last 10 ns of the MD run.…”

In Silico Modeling and Structural Analysis of Soluble Epoxide Hydrolase Inhibitors for Enhanced Therapeutic Design

“…In this work, to determine the applicability domain (AD) of the 2D-QSAR models, a leverage estimation approach was followed, and the Williams plot generated. The Williams plot displays the leverage, which measures the influence of individual data points, against the standardized residuals [46,[52][53][54]. This plot helps in identifying structural and response outliers in the linear 2D-QSAR models.…”

“…After the minimization, the ligand structures were used to generate 100 conformations using the rdMolAlign.GetCrippenO3A code of Rdkit. The Python script "alignment.py" written and used for the atom-based alignment can be found in the GitHub repository: https: //github.com/ncordeirfcup/InsilicoModeling_RdRp, (accessed on 25 May 2023) [52].…”