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Computational study reveals that ACE2 missense mutations disrupt protein stability and weaken angiotensin II binding, suggesting a mechanistic link to Alzheimer disease vulnerability.
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by complex molecular mechanisms extending beyond amyloid-β aggregation and tau pathology. Increasing evidence implicates dysregulation of the renin-angiotensin system in AD, with angiotensin-converting enzyme 2 (ACE2) playing a critical regulatory role. However, the structural and functional consequences of ACE2 genetic polymorphisms in Alzheimer's disease remain insufficiently explored. In this study, a comprehensive in silico framework was employed to investigate the impact of ACE2 missense variants on protein stability, dynamics, and interaction with angiotensin II. Missense single nucleotide polymorphisms (SNPs) were retrieved from the dbSNP and gnomAD databases and annotated using the Ensembl Variant Effect Predictor. A multi-tiered screening strategy integrating SIFT, PANTHER, PhD-SNP, SNPs&GO, FoldX, DynaMut2, and mCSM was applied. Based on consensus predictions, four high-risk variants (F308S, L186S, Y654S, and L570S) were selected for structural analysis. Molecular dynamics simulations revealed that all selected variants induced varying degrees of structural destabilization compared to wild-type ACE2 protein. Mutant proteins exhibited increased backbone deviation, enhanced residue-level flexibility, reduced compactness, elevated solvent exposure, weakened intramolecular interactions, broader conformational sampling, and reduced energetic stability. The L570S showing the most pronounced effects, increased structural fluctuations, reduced complex stability and progressively weaker binding free energies. Protein-peptide docking demonstrated reduced interaction scores for angiotensin II across all mutant variants. Overall, this study provides mechanistic insights into how ACE2 polymorphisms may disrupt protein stability and impair angiotensin II recognition, offering a computational basis for understanding their potential contribution to Alzheimer's disease susceptibility and supporting future experimental validation.
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Eid et al. (2026) studied this question.
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