Computational Approaches to Evaluate the Acetylcholinesterase Binding Interaction with Taxifolin for the Management of Alzheimer’s Disease

Abstract

Acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) are enzymes that break down and reduce the level of the neurotransmitter acetylcholine (ACh). This can cause a variety of cognitive and neurological problems, including Alzheimer’s disease. Taxifolin is a natural phytochemical generally found in yew tree bark and has significant pharmacological properties, such as being anti-cancer, anti-inflammatory, and antioxidant. The binding affinity and inhibitory potency of taxifolin to these enzymes were evaluated through molecular docking and molecular dynamics simulations followed by the MMPBSA approach, and the results were significant. Taxifolin’s affinity for binding to the AChE–taxifolin complex was −8.85 kcal/mol, with an inhibition constant of 326.70 nM. It was observed to interact through hydrogen bonds. In contrast, the BChE–taxifolin complex binding energy was observed to be −7.42 kcal/mol, and it was significantly nearly equal to the standard inhibitor donepezil. The molecular dynamics and simulation signified the observed interactions of taxifolin with the studied enzymes. The MMPBSA total free energy of binding for AChE–taxifolin was −24.34 kcal/mol, while BChE–taxifolin was −16.14 kcal/mol. The present research suggests that taxifolin has a strong ability to bind and inhibit AChE and BChE and could be used to manage neuron-associated problems; however, further research is required to explore taxifolin’s neurological therapeutic potential using animal models of Alzheimer’s disease.

Insights into the stereoisomerism of dihydroquercetin: analytical and pharmacological aspects

“…The opportunities for using DHQ in the therapy of socially significant diseases (Alzheimer’s disease and COVID-19) are increasingly in focus of research ( Zhu et al, 2022 ; Hattori et al, 2023 ; Saxena et al, 2024 ). Furthermore, new computational methods of analysis have been implemented in research practice ( Terekhov et al, 2019 ; Rudrapal et al, 2022 ; Ahmad et al, 2024 ).…”