Integrative bioinformatics analysis of APOE variants in Alzheimer's disease and clinical therapeutics.

In this study, a comprehensive bioinformatics workflow is employed to investigate the impact of APOE gene variants on Alzheimer's disease (AD) and to explore their relevance for improving therapeutic strategies. Multiple databases were screened to identify key non-synonymous single nucleotide polymorphisms (nsSNPs) in APOE. Six variants: rs769452 (L46P), rs429358 (C130R), rs267606664 (G145D), rs121918393 (R154S), rs7412 (R176C), and rs267606661 (R269G) were selected, of which five were predicted to be deleterious. Given its high interaction score (0.789), the FDA-approved AD drug Donepezil was chosen as the ligand to assess binding with both wild-type and mutant APOE proteins. Structural modeling using AlphaFold3 generated high-quality APOE structures, and in silico mutagenesis revealed mutation-dependent destabilization. AutoDock4 molecular docking was performed to evaluate binding affinities of Donepezil with the predicted active-site residues of wild-type and mutant APOE. Furthermore, 100 ns molecular dynamics simulations using AMBER20 were conducted for all APOE-Donepezil complexes. Analyses of RMSD, RMSF, and radius of gyration indicated overall structural stability, residue-level flexibility, and protein compactness throughout the simulations. Interaction profiling revealed stable hydrophobic contacts and hydrogen bonds in both wild-type and mutant complexes. Our findings suggest that structural variations arising from APOE genotypes may modulate Donepezil binding and potentially influence therapeutic response in AD patients. However, these computational predictions require validation through biophysical assays, cellular experiments, and genotype-stratified clinical studies. Integrating molecular modeling with experimental research will be essential for advancing APOE-guided precision medicine and optimizing Donepezil therapy for Alzheimer's disease.