Integrative computational approaches for designing novel alpha-glucosidase inhibitors based on curculigoside A derivatives: Virtual screening, molecular docking, and molecular dynamics

Abstract

Introduction: Over 422 million people worldwide suffer from diabetes, causing 1.5 million fatalities annually. The existing medications have shortcomings, including poor glucose control and adverse effects. The present study aimed to create possible alpha-glucosidase inhibitors utilizing a curculigoside A derivative ligand-based model. Methods: A pharmacophore model was constructed utilizing the structure information of curculigoside A derivatives and PharmaGist. Subsequently, virtual screening, molecular docking, and molecular dynamics were employed to simulate the hits. Results: From six training sets, eleven pharmacophore models were developed; the model with the highest score (18.0435) was chosen for further analysis. Using the verified pharmacophore model, 270 547 chemicals from the ZINC natural product database were subjected to virtual screening. Subsequently, molecular docking was performed on the top 1000 hits with AutoDock Wizard from PyRx. This analysis unveiled 434 hits with better binding energies than acarbose, the native ligand. Subsequently, second optimal docking configurations were evaluated with AutoDock 2.4; this process yielded the discovery of three prospective inhibitors (ZINC000150350051, ZINC000008382292, and ZINC000085595291) characterized by the most advantageous binding energies. To evaluate the stability and dynamics of these ligand-receptor complexes, Gromacs 2022 molecular dynamics simulations were executed for one hundred nanoseconds. Out of the three hits, ZINC000085595291 (Hit03) exhibited good energy components and interaction stability constantly during the simulation. Conclusion: The integrated computational strategy identified promising alpha-glucosidase inhibitors in curculigoside A compounds, highlighting the potential of ZINC000085595291 (Hit03) as a potential diabetes therapeutic agent.