Enhancing aptamer selection in alzheimer’s disease: integrating structure prediction and molecular dynamics simulations

Abstract

Abstract Alzheimer’s disease is the most frequent neurodegenerative disease and the leading cause of dementia worldwide. With disease-modifying treatments highly requested, numerous aptamers have been experimentally selected, showing high affinity and specificity binding to the main drivers in the pathology. Still, more studies are needed to compare the biochemical properties and target interactions to streamline the generation of high-efficacy therapeutics. With recent improvements in bioinformatics, we predicted the 2D and 3D structures of known aptamers based on literature-derived sequences, followed by molecular dynamics, molecular docking, and MM/PBSA binding affinity simulations of the aptamer-target complexes. We observed a strong correlation between experimental affinity values and predicted binding free energies, demonstrating the value of implementing computational strategies to streamline the selection process. We identified DNA aptamers as most promising due to their high predictability compared to RNA aptamers and the low docking scores of peptide aptamers. Furthermore, we identified hydrophobic and basic amino acids most frequently contributing to the interaction, with the basic amino acids, arginine, histidine, and lysine accounting for most interactions in all groups. This suggests that forming hydrophobic pockets and ionic interactions mediates aptamer binding, allowing a more directed targeting of Alzheimer’s disease and providing the basis for future modifications.

Keywords

• Alzheimer’s disease
• Aptamer
• Structure prediction
• Molecular dynamics
• Molecular Docking
• MM/PBSA