Investigating the Potential Anti-Alzheimer's Disease Mechanism of Marine Polyphenols: Insights from Network Pharmacology and Molecular Docking.
Kumju YounChi-Tang HoMira JunPublished in: Marine drugs (2023)
Marine polyphenols, including eckol(EK), dieckol(DK), and 8,8'-bieckol(BK), have attracted attention as bioactive ingredients for preventing Alzheimer's disease (AD). Since AD is a multifactorial disorder, the present study aims to provide an unbiased elucidation of unexplored targets of AD mechanisms and a systematic prediction of effective preventive combinations of marine polyphenols. Based on the omics data between each compound and AD, a protein-protein interaction (PPI) network was constructed to predict potential hub genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to provide further biological insights. In the PPI network of the top 10 hub genes, AKT1, SRC, EGFR, and ESR1 were common targets of EK and BK, whereas PTGS2 was a common target of DK and BK. GO and KEGG pathway analysis revealed that the overlapped genes between each compound and AD were mainly enriched in EGFR tyrosine kinase inhibitor resistance, the MAPK pathway, and the Rap1 and Ras pathways. Finally, docking validation showed stable binding between marine polyphenols and their top hub gene via the lowest binding energy and multiple interactions. The results expanded potential mechanisms and novel targets for AD, and also provided a system-level insight into the molecular targets of marine polyphenols against AD.
Keyphrases
- protein protein
- bioinformatics analysis
- genome wide
- genome wide identification
- molecular docking
- small molecule
- small cell lung cancer
- network analysis
- tyrosine kinase
- genome wide analysis
- signaling pathway
- molecular dynamics simulations
- copy number
- dna methylation
- human health
- cognitive decline
- single cell
- molecular dynamics
- transcription factor
- machine learning
- oxidative stress
- climate change
- big data
- estrogen receptor
- deep learning