Defining the Role of Isoeugenol from Ocimum tenuiflorum against Diabetes Mellitus-Linked Alzheimer’s Disease through Network Pharmacology and Computational Methods

Abstract

The present study involves the integrated network pharmacology and phytoinformatics-based investigation of phytocompounds from Ocimum tenuiflorum against diabetes mellitus-linked Alzheimer’s disease. It aims to investigate the mechanism of the Ocimum tenuiflorum phytocompounds in the amelioration of diabetes mellitus-linked Alzheimer’s disease through network pharmacology, druglikeness and pharmacokinetics, molecular docking simulations, GO analysis, molecular dynamics simulations, and binding free energy analyses. A total of 14 predicted genes of the 26 orally bioactive compounds were identified. Among these 14 genes, GAPDH and AKT1 were the most significant. The network analysis revealed the AGE-RAGE signaling pathway to be a prominent pathway linked to GAPDH with 50.53% probability. Upon the molecular docking simulation with GAPDH, isoeugenol was found to possess the most significant binding affinity (−6.0 kcal/mol). The molecular dynamics simulation and binding free energy calculation results also predicted that isoeugenol forms a stable protein–ligand complex with GAPDH, where the phytocompound is predicted to chiefly use van der Waal’s binding energy (−159.277 kj/mol). On the basis of these results, it can be concluded that isoeugenol from Ocimum tenuiflorum could be taken for further in vitro and in vivo analysis, targeting GAPDH inhibition for the amelioration of diabetes mellitus-linked Alzheimer’s disease.

Phyto-Computational Intervention of Diabetes Mellitus at Multiple Stages Using Isoeugenol from Ocimum tenuiflorum: A Combination of Pharmacokinetics and Molecular Modelling Approaches

“…The binding free energy calculation of the complex was estimated using the mechanics/Poisson–Boltzmann surface area (MM–PBSA) approach, using the g_mmpbsa program, which is a GROMACS plugin. The quantitatively estimated of MM–PBSA was performed according to the study conducted by Martiz et al (2022) [ 30 ]. The calculation was performed using the last 50 ns frames, which were extracted from the MD trajectory [ 18 , 19 ].…”

Discovery of Novel Coumarin Derivatives as Potential Dual Inhibitors against α-Glucosidase and α-Amylase for the Management of Post-Prandial Hyperglycemia via Molecular Modelling Approaches

“…∆G = ∆ EMM + ∆G Solvation − T∆S = ∆E (Bonded + non-bonded) + ∆G (Polar + non-polar) − T∆S (2) G Binding : binding free energy, G Complex : total free energy of the protein-ligand complex, G Protein and G Ligand : total free energies of the isolated protein and ligand in solvent, respectively, ∆G: standard free energy, ∆ EMM : average molecular mechanics potential energy in vacuum, G Solvation : solvation energy, ∆E: total energy of bonded as well as non-bonded interactions, ∆S: change in entropy of the system upon ligand binding, and T: temperature in Kelvin [27,28].…”

Synthesis, Characterization, Hirshfeld Surface Analysis, Crystal Structure and Molecular Modeling Studies of 1-(4-(Methoxy(phenyl)methyl)-2-methylphenoxy)butan-2-one Derivative as a Novel α-Glucosidase Inhibitor

“…This is an efficient and reliable free energy simulation method used to model molecular recognition, such as for protein-ligand binding interactions. A GROMACS program, g_mmpbsa [21] with the MmPbSaStat.py [22] script was exploited to evaluate the binding free energy for each protein-ligand complex. The g_mmpbsa program calculates binding free energy using three components: molecular mechanical energy, polar and apolar solvation energies, and molecular mechanical energy.…”