Investigating the molecular mechanisms of the "Tianma-Gouteng" herb pair in treating Parkinson's disease: a bioinformatics approach and density functional theory with molecular dynamics simulations validation.
This in silico study uses network pharmacology, molecular docking, 100-ns MD simulations, and DFT to nominate quercetin and kaempferol from the Tianma-Gouteng herb pair as stable binders of AKT1, TP53, and STAT3, linking PI3K-AKT signaling, mitochondrial apoptosis, and neuroinflammation to…
What the AI sees
This in silico study uses network pharmacology, molecular docking, 100-ns MD simulations, and DFT to nominate quercetin and kaempferol from the Tianma-Gouteng herb pair as stable binders of AKT1, TP53, and STAT3, linking PI3K-AKT signaling, mitochondrial apoptosis, and neuroinflammation to…
Research significance
It generates disease-relevant, actionable hypotheses by prioritizing well-known flavonoids with predicted stable interactions to PD-relevant targets—making them tractable candidates for biochemical and in vivo validation—though findings are limited by being purely computational.
Source abstract
Parkinson's disease (PD) is a complex neurodegenerative disorder for which current treatments are often symptomatic and lack disease-modifying effects. The traditional Chinese medicine herb pair Tianma-Gouteng, composed of Gastrodia elata Bl (Tianma) and Uncaria rhynchophylla(Miq.) Miq. ex Havil. (Gouteng), has demonstrated clinical efficacy in treating PD motor symptoms, yet its multi-target mechanisms remain unclear. This study employs an integrated approach combining bioinformatics and computational chemistry to elucidate these mechanisms and identify key active components. Methods involved network pharmacology to identify active compounds and PD-related targets, followed by protein-protein interaction network analysis and functional enrichment. Molecular docking and 100-ns molecular dynamics (MD) simulations were utilized to evaluate the binding stability and dynamics of core component-target complexes. Additionally, Density Functional Theory (DFT) was conducted to analyze the electronic properties and reactivity of key compounds. Network pharmacology analysis identified 42 active components and 261 PD-related targets. Core targets identified were AKT1, TP53, and STAT3, which are involved in the regulation of PI3K-AKT signaling, mitochondrial apoptosis, and neuroinflammation. MD simulations demonstrated that quercetin (QU) and kaempferol (KA) formed highly stable complexes with AKT1 and TP53, exhibiting low average root-mean-square deviation (RMSD <0.2 nm), stable radius of gyration (Rg fluctuation <0.05 nm), and sustained protein-ligand hydrogen bonds. In contrast, complexes with 4-4'-hydroxybenzyloxy and 20-hexadecanoylingenol showed conformational instability, consistent with higher entropy penalties. DFT calculations revealed that QU and KA possess low HOMO-LUMO gaps, indicating high chemical reactivity, along with strong nucleophilic regions and intramolecular hydrogen bonds that facilitate target binding. The Tianma-Gouteng pair exerts anti-PD effects through the synergistic modulation of AKT1-mediated PI3K-AKT signaling, STAT3-driven neuroinflammation, and TP53-regulated apoptosis. Quercetin and kaempferol are identified as pivotal components due to their stable target binding and favorable electronic properties, providing a promising foundation for the development of novel PD therapeutics.