(770h) Computational Studies on Amyloid Disassembly Using Molecular Dynamics Simulations and Free Energy Calculations

The self-assembly of peptides and proteins into amyloid fibrils serves a key role in several diseases, underlining the importance of understanding the fundamentals of amyloid self-assembly. Particularly, the self-assembly of amyloid β(Aβ) into senile plaques have been widely studied due to its connection to Alzheimer’s disease (AD). [1,2] Alzheimer’s disease (AD) is characterized by deposition of Aβ plaques and neurofibrillary tangles in the brain, accompanied by synaptic dysfunction and neurodegeneration. Thus, the inhibition of Aβ self-assembly has been the focus of several experimental and computational studies during the last decades to develop therapeutic strategies for Alzheimer’s disease. [3,4] Despite the invaluable insights deduced by such studies, and additional computational studies investigating the formation of Αβ assemblies [including 5-10] or sequestration [11-13], we still lack complete knowledge on the structural, dynamical properties, and in summary the driving forces, leading to amyloid self-assembly, especially considering the complexity possible structural arrangements that can be formed [7-10].

Recent experimental studies highlight the importance of investigating Αβ amyloid disassembly as a potential therapeutic avenue for the recovery of AD-related behavioral deficits [14,15]. Motivated by the importance of studying Αβ amyloid disassembly as a potential therapeutic avenue, and given that current experimental methods cannot study the effect in atomic detail at pico- or nano-second resolution, we employed a series of molecular dynamics (MD) simulations and free energy calculations to investigate Αβ amyloid disassembly by several compounds with known experimental disassembling capacities. Our results comply with experiments and show the key structural and dynamic components leading to disassembly, and highlight the key interactions that lead to Αβ peptide dissociation. In addition, our studies suggest that understanding amyloid disassembly and the key driving forces leading to it can provide additional insights into the key determinants driving self-assembly, which can be seen as amyloid disassembly “in reverse”.

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