The Identification of Gyrophoric Acid, a Phytochemical Derived from Lichen, as a Potent Inhibitor for Aggregation of Amyloid Beta Peptide: In Silico and Biochemical Evaluation
Alzheimer's disease (AD) is characterized by amyloid-beta (Aβ) plaque accumulation and neurodegeneration. This study identified gyrophoric acid, a lichen-derived phenolic metabolite, as a dual-action Aβ42 inhibitor preventing aggregation and disassembling of mature Aβ42 fibrils. Integrated in silico studies revealed that gyrophoric acid was a strong thermodynamic stabilizer of Aβ42 (MM-GBSA: -27.3 kcal/mol) via entropically driven hydrophobic interactions and disruption of aggregation-prone conformations (100 ns MD simulations). Through biochemical analysis of the fluorescent dye thioflavin T (ThT), gyrophoric acid induced rapid Aβ42 fibril disassembly within 5 h, with time-lapse confocal microscopy quantitatively confirming the near-complete dissolution of large aggregates by 24 h. ADMET profiling revealed favorable pharmacokinetics (moderate oral absorption: 48.5-57.3%; low toxicity) and Lipinski's rule compliance. These results establish gyrophoric acid as a promising natural bioactive compound for anti-AD therapeutics with a unique hydrophobic-stabilization mechanism.
Authors
- Meixia Yang (ORCID: https://orcid.org/0009-0001-9910-6133)
- Karl Wah Keung Tsim (ORCID: https://orcid.org/0000-0003-4808-1674)
- Ka Wing Leung (ORCID: https://orcid.org/0000-0002-6137-3877)
- Jin Gao (ORCID: https://orcid.org/0000-0003-3199-6632)
- Queenie Wing Sze Lai (ORCID: https://orcid.org/0000-0002-5964-608X)
- Farkhod Eshboev (ORCID: https://orcid.org/0000-0003-3054-6111)
- Qin Xu (ORCID: https://orcid.org/0000-0001-6214-1149)
- Haitao Hu (ORCID: https://orcid.org/0000-0001-8496-5704)
- Tina Ting-Xia Dong
Institutions
- Academy of Sciences Republic of Uzbekistan (UZ)
- Xuzhou Medical College (CN)
- Hong Kong University of Science and Technology (HK)
- HKUST Shenzhen Research Institute (CN)
- Yunusov Institute of the Chemistry of Plant Substances (UZ)
Publication Details
- Journal
- International Journal of Molecular Sciences
- Published
- 2025-09-01
- DOI
- https://doi.org/10.3390/ijms26178500
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Science, Technology and Innovation Commission of Shenzhen Municipality
- Zhongshan Science and Technology Bureau