Exploring the Potential of Natural Products as FoxO1 Inhibitors: an In Silico Approach

Biomol Ther (Seoul). 2024 May 1;32(3):390-398. doi: 10.4062/biomolther.2023.156. Epub 2024 Apr 9.

Abstract

FoxO1, a member of the Forkhead transcription factor family subgroup O (FoxO), is expressed in a range of cell types and is crucial for various pathophysiological processes, such as apoptosis and inflammation. While FoxO1's roles in multiple diseases have been recognized, the target has remained largely unexplored due to the absence of cost-effective and efficient inhibitors. Therefore, there is a need for natural FoxO1 inhibitors with minimal adverse effects. In this study, docking, MMGBSA, and ADMET analyses were performed to identify natural compounds that exhibit strong binding affinity to FoxO1. The top candidates were then subjected to molecular dynamics (MD) simulations. A natural product library was screened for interaction with FoxO1 (PDB ID- 3CO6) using the Glide module of the Schrödinger suite. In silico ADMET profiling was conducted using SwissADME and pkCSM web servers. Binding free energies of the selected compounds were assessed with the Prime-MMGBSA module, while the dynamics of the top hits were analyzed using the Desmond module of the Schrödinger suite. Several natural products demonstrated high docking scores with FoxO1, indicating their potential as FoxO1 inhibitors. Specifically, the docking scores of neochlorogenic acid and fraxin were both below -6.0. These compounds also exhibit favorable drug-like properties, and a 25 ns MD study revealed a stable interaction between fraxin and FoxO1. Our findings highlight the potential of various natural products, particularly fraxin, as effective FoxO1 inhibitors with strong binding affinity, dynamic stability, and suitable ADMET profiles.

Keywords: ADMET screening; Docking; FoxO1; In silico; MMGBSA; Molecular dynamics.

Grants and funding

ACKNOWLEDGMENTS The authors extend their appreciation to the Research Supporting project, funded by the National Research Foundation of Republic of Korea (NRF) under the Ministry of Education (grant number: 2021R1A6A1A-03044296). Additionally, this research received support from the Chung-Ang University Research Grant in 2023.