» Articles » PMID: 39846080

Identification of Promising Dipeptidyl Peptidase-4 and Protein Tyrosine Phosphatase 1B Inhibitors from Selected Terpenoids Through Molecular Modeling

Overview
Journal Bioinform Adv
Date 2025 Jan 23
PMID 39846080
Authors
Affiliations
Soon will be listed here.
Abstract

Motivation: Investigating novel drug-target interactions is crucial for expanding the chemical space of emerging therapeutic targets in human diseases. Herein, we explored the interactions of dipeptidyl peptidase-4 and protein tyrosine phosphatase 1B with selected terpenoids from African antidiabetic plants.

Results: Using molecular docking, molecular dynamics simulations, molecular mechanics with generalized Born and surface area solvation-free energy, and density functional theory analyses, the study revealed dipeptidyl peptidase-4 as a promising target. Cucurbitacin B, 6-oxoisoiguesterin, and 20-epi-isoiguesterinol were identified as potential dipeptidyl peptidase-4 inhibitors with strong binding affinities. These triterpenoids interacted with key catalytic and hydrophobic pockets of dipeptidyl peptidase-4, demonstrating structural stability and flexibility under dynamic conditions, as indicated by dynamics simulation parameters. The free energy analysis further supported the binding affinities in dynamic environments. Quantum mechanical calculations revealed favorable highest occupied molecular orbital and lowest unoccupied molecular orbital energy profiles, indicating the suitability of the hits as proton donors and acceptors, which likely enhance their molecular interactions with the targets. Moreover, the terpenoids showed desirable drug-like properties, suggesting their potential as safe and effective dipeptidyl peptidase-4 inhibitors. These findings may pave the way for the development of novel antidiabetic agents and nutraceuticals based on these promising hits.

Availability And Implementation: Not applicable.

References
1.
Pilacinski S, Zozulinska-Ziolkiewicz D . Influence of lifestyle on the course of type 1 diabetes mellitus. Arch Med Sci. 2014; 10(1):124-34. PMC: 3953982. DOI: 10.5114/aoms.2014.40739. View

2.
Ogunyemi O, Gyebi G, Ibrahim I, Esan A, Olaiya C, Soliman M . Identification of promising multi-targeting inhibitors of obesity from Vernonia amygdalina through computational analysis. Mol Divers. 2022; 27(1):1-25. DOI: 10.1007/s11030-022-10397-6. View

3.
Morris G, Huey R, Lindstrom W, Sanner M, Belew R, Goodsell D . AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem. 2009; 30(16):2785-91. PMC: 2760638. DOI: 10.1002/jcc.21256. View

4.
Miller 3rd B, McGee Jr T, Swails J, Homeyer N, Gohlke H, Roitberg A . MMPBSA.py: An Efficient Program for End-State Free Energy Calculations. J Chem Theory Comput. 2015; 8(9):3314-21. DOI: 10.1021/ct300418h. View

5.
Liang L, Gao L, Li J, Taglialatela-Scafati O, Guo Y . Cembrane diterpenoids from the soft coral Sarcophyton trocheliophorum Marenzeller as a new class of PTP1B inhibitors. Bioorg Med Chem. 2013; 21(17):5076-80. DOI: 10.1016/j.bmc.2013.06.043. View