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Updated: Jan 16, 2026

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
Published on: June 20, 2025
Targeting vascular dementia: Molecular docking and dynamics of natural ligands against neuroprotective proteins
Zhizhong Wang1,2, Sen Xu3, Ailong Lin2
1College of Chinese Medicine, Hubei University of Chinese Medicine, Wuhan, Hubei, China.
Abstract:
Vascular dementia (VaD), a neurodegenerative disease driven by vascular pathology, requires multi-targeted therapeutic strategies. This study employs an integrated in silico approach to evaluate the neuroprotective potential of natural ligands against key proteins implicated in VaD pathogenesis. Using molecular docking and normal mode analysis (NMA), four natural compounds (Galangin, Resveratrol, Curcumin, and Licocumarone) were assessed for their binding affinity and structural influence on six target proteins: APLP1, APOE, CLDN5, SOD1, MMP9, and MTHFR. Docking analysis revealed that galangin exhibited the highest binding affinity to APLP1 (-8.5 kcal/mol), resveratrol to MTHFR (-8.1 kcal/mol), and curcumin showed dual efficacy toward APOE (-7.2 kcal/mol) and MMP9 (-8.0 kcal/mol). Licocumarone demonstrated notable stabilization of CLDN5 and SOD1. The NMA results indicated ligand-induced stabilization of protein cores and enhanced flexibility in loop regions, which may impact amyloid aggregation, oxidative stress, and blood-brain barrier integrity. Pathway enrichment using the KEGG and Reactome databases identified significant involvement of the IL-17 and TNF signaling pathways, along with leukocyte transendothelial migration, linking inflammation with vascular dysfunction. APOE emerged as a central node within the protein-protein interaction network, highlighting its regulatory importance. This study highlights the therapeutic relevance of natural ligands as cost-effective modulators of multiple VaD-associated pathways. The combined use of molecular docking, protein dynamics, and enrichment analyses provides a comprehensive computational framework for early-stage drug discovery. These findings warrant further experimental validation to advance the development of targeted, mechanism-driven interventions for vascular dementia.
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