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<p>Ginsenoside Rb1 was identified as the most effective destabilizer of the amyloid-beta (Aβ) protofibril in this study. Four major ginsenosides, Compound K (CK), Rb1, Re, and Rg1 were evaluated for their ability to disrupt Aβ protofibrils using molecular docking and molecular dynamics (MD) simulations. Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by the accumulation of Aβ peptides into toxic oligomers and fibrillar aggregates, leading to synaptic dysfunction and neuronal death. Targeting Aβ aggregation is therefore a promising therapeutic strategy, and naturally derived compounds such as ginsenosides offer advantages in terms of biocompatibility and potentially reduced side effects. Docking analyses revealed favorable binding affinities for all tested ligands, with Rb1 exhibiting the strongest interactions, primarily mediated by hydrogen bonding and hydrophobic contacts with key residues of the Aβ protofibril. MD simulations further demonstrated that Rb1 induced pronounced structural destabilization, as evidenced by increased root mean square deviation (RMSD), radius of gyration (Rg), solvent-accessible surface area (SASA), and inter-chain distances. These alterations indicate effective disruption of β-sheet packing and protofibril integrity. In comparison, CK, Re, and Rg1 exerted moderate to mild destabilizing effects, although Re also showed notable disruption of β-sheet organization. Collectively, these computational findings highlight ginsenoside Rb1 as a promising natural compound for interfering with Aβ aggregation. The study underscores the value of in silico approaches in elucidating molecular mechanisms and guiding early-stage therapeutic development, while emphasizing the need for experimental validation to confirm the translational potential of ginsenoside-based interventions for AD.</p> Graphical abstract <p></p>

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Mechanistic insights into amyloid β fibril disruption by ginsenosides through molecular dynamics simulations

  • 期刊论文

  • 发表日期：

    2026年2月2日

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  • Sium Ahmed,
  • Sagar Sivaji Barale,
  • Rupali Sarjerao Kamble,
  • Kaniz Fatema,
  • Kailas Dashrath Sonawane,
  • Kathleen Hefferon,
  • Kee Yoeup Paek,
  • Abdullah Mohammad Shohael

摘要

Ginsenoside Rb1 was identified as the most effective destabilizer of the amyloid-beta (Aβ) protofibril in this study. Four major ginsenosides, Compound K (CK), Rb1, Re, and Rg1 were evaluated for their ability to disrupt Aβ protofibrils using molecular docking and molecular dynamics (MD) simulations. Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by the accumulation of Aβ peptides into toxic oligomers and fibrillar aggregates, leading to synaptic dysfunction and neuronal death. Targeting Aβ aggregation is therefore a promising therapeutic strategy, and naturally derived compounds such as ginsenosides offer advantages in terms of biocompatibility and potentially reduced side effects. Docking analyses revealed favorable binding affinities for all tested ligands, with Rb1 exhibiting the strongest interactions, primarily mediated by hydrogen bonding and hydrophobic contacts with key residues of the Aβ protofibril. MD simulations further demonstrated that Rb1 induced pronounced structural destabilization, as evidenced by increased root mean square deviation (RMSD), radius of gyration (Rg), solvent-accessible surface area (SASA), and inter-chain distances. These alterations indicate effective disruption of β-sheet packing and protofibril integrity. In comparison, CK, Re, and Rg1 exerted moderate to mild destabilizing effects, although Re also showed notable disruption of β-sheet organization. Collectively, these computational findings highlight ginsenoside Rb1 as a promising natural compound for interfering with Aβ aggregation. The study underscores the value of in silico approaches in elucidating molecular mechanisms and guiding early-stage therapeutic development, while emphasizing the need for experimental validation to confirm the translational potential of ginsenoside-based interventions for AD.

Graphical abstract