Session: Physical Chemistry Poster:

Alzheimer’s disease (AD) is a neurodegenerative disease characterized by extracellular amyloid-β (Aβ) plaques that form toxic aggregates and lesions. The underlying cause of AD is unknown, but studies have shown that familial AD (FAD) mutations of Aβ lead to early-onset and a more severe form of the disease. FAD mutations such as D23N, E22G, E22K, and E22Q have charge-altering mutations that aggregate quicker than wild-type (WT). The mechanism by which these mutations facilitate increased aggregation is unknown. To gain a greater understanding of how these specific mutations lead to a more severe form of the disease, we performed polarizable molecular dynamics (MD) simulations using the Drude-2019 force field to investigate the differences between the WT and mutants. The polarizable force field allows us to gain insights into how electrostatic interactions, including those between permanent and induced dipoles, affect side-chain properties, salt bridges, water, and ion interactions. We performed simulations on the full-length Aβ1-42 of WT and FAD mutations to understand how these mutations alter the secondary and tertiary structure. We found that the WT and mutants each adopt unique secondary and tertiary contacts which lead to changes in the electrostatic structure of the peptides. To further understand how FAD mutants alter structure we performed umbrella sampling simulations on the Aβ15-27 fragment to quantify how each mutant alters the unfolding free energy of the peptide. In addition, we ran US at various temperatures to determine the enthalpy and entropy of each system. We found that the WT and E22 mutants have similar unfolding free energies and unfolding pathways but the D23N mutant unfolds differently. Furthermore, the D23N mutant is more entropically driven where the WT and E22 mutants are enthalpically driven. By studying both full-length Aβ and fragment, we can systematically study how these FAD mutations change the free energy of folding and secondary and tertiary structure.

Darcy S Davidson

Ph.D. Candidate, Virginia Tech

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