Identification of flavonoids from natural products against hyperlipidaemia through computational approaches
Over the centuries, hyperlipidaemia has been recognised as a major risk factor contributing to cardiovascular disease (CVD). Current research increasingly explores natural compounds such as flavonoids for their potential lipid-lowering effects. This study evaluated pharmacological potential of flavonoids to identify strong inhibitors for target receptor, Proprotein Convertase Subtilisin/Kexin Type (PCSK9). A total of 29 compounds, including one FDA-approved drugs were screened for drug-likeness and ADMET properties, followed by molecular docking and Molecular Dynamics (MD) simulations using PCSK9 receptor containing glucose and sodium (PDB ID: 5OCA). This complex was chosen due to their functional relevance to cholesterol metabolism. As a results, 24 compounds satisfied Lipinski’s Rule of Five and exhibited favourable drug-likeness and toxicity profiles. Simvastatin (control) was discovered to bind into the pocket B binding site along with seven flavonoids; Diosmetin, Eupatorin, Isorhamnetin, Myricetin, Petunidin, Sinensetin, and Tilianin. These flavonoids displayed docking energies ranging from −8.0 to −10.2 kcal/mol. Tilianin showed the strongest affinity (−10.2 kcal/mol), while Simvastatin bound with −9.1 kcal/mol. MD simulations confirmed that the PCSK9-ligand complexes remained structurally stable throughout a 100 ns trajectory. MM/PBSA analysis further supported the binding interactions, revealing free energies values ranging from −17 to −30.41 kcal/mol. Among the flavonoids evaluated, Tilianin, Myricetin, and Sinensetin demonstrate the most favourable interaction profiles. These finding suggest that the selected flavonoids have strong potential as promising anti-hyperlipidaemic candidates targeting PCSK9.
Authors
- Ernie Zuraida Ali (ORCID: https://orcid.org/0000-0001-9884-7389)
- Nurul Azira Ismail (ORCID: https://orcid.org/0000-0001-5368-0810)
- Nurmala Kamal
Institutions
- Management and Science University (MY)
- National Institutes of Biotechnology Malaysia (MY)
Publication Details
- Journal
- PLoS ONE
- Published
- 2026-10-09
- DOI
- https://doi.org/10.1371/journal.pone.0346073
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00