# Integrative GC-MS, network pharmacology, and molecular dynamics elucidate synergistic anti-diabetic mechanisms of Chongqing Citrus reticulata ‘Dahongpao’ volatile oil via multi-target stabilization

## TL;DR

This study uses chemical analysis and computer modeling to show how a citrus oil from Chongqing may help treat diabetes by affecting multiple biological targets.

## Contribution

The novel integration of GC-MS, network pharmacology, and molecular dynamics reveals synergistic anti-diabetic mechanisms of a citrus volatile oil.

## Key findings

- GC-MS identified 82 compounds in the citrus oil, with D-limonene as the main component.
- Molecular docking and MD simulations confirmed strong binding of thymol and n-hexadecanoic acid to diabetes-related proteins.
- Pathway analysis linked the oil's effects to PPAR signaling and insulin resistance pathways.

## Abstract

Diabetes mellitus involves complex pathogenesis requiring multi-target interventions. Citrus reticulata ‘Dahongpao’ from Chongqing exhibits anti-diabetic potential, but its mechanisms remain elusive.

We employed an integrative strategy: GC-MS identified 82 compounds (96.61% coverage), dominated by D-limonene (62.48%). Network pharmacology revealed 36 diabetes-related targets. Molecular docking prioritized ligands (thymol: −6.8 kcal/mol with FABP1; n-hexadecanoic acid: −6.7 kcal/mol with PTGS2). Critical validation was achieved via 100-ns molecular dynamics (MD) simulations and MM-GBSA binding free energy calculations.

MD simulations demonstrated structural stability (RMSD < 2.5 Å) for core complexes (e.g., CYP19A1/thymol). MM-GBSA quantified robust binding for FABP1/dodecanoic acid (−43.26 kcal/mol) and PTGS2/n-hexadecanoic acid (−43.93 kcal/mol), driven by van der Waals forces. Hydrogen bond dynamics revealed persistent interactions (e.g., thymol–THR102 in FABP1), while RMSF highlighted ligand-induced flexibility in fatty acids. Pathway analysis implicated PPAR signaling and insulin resistance.

Citrus reticulata ‘Dahongpao’ essential oil combats diabetes through synergistic multi-target modulation, validated by dynamic ligand–protein stability and energetics. This study presents an in silico framework that integrates phytochemical profiling and computational analyses to facilitate natural product drug discovery.