Probing Amyloid Fibril Self-Assembly with Network Hamiltonian Simulations in Explicit Space

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Project Summary/Abstract Amyloid fibril formation is central to the disease etiology of a number of human diseases, including Alzheimer’s disease, type 2 diabetes, and a variety of prion diseases. Although molecular structures for thousands of amyloid fibrils have been resolved using techniques like X-ray crystallography and nuclear magnetic resonance (NMR), the mechanism of amyloid fibril formation is largely unknown. The mechanism of primary nucleation, whereby fibril formation begins in a solvent environment that previously did not contain any amyloid fibrils, a crucial step in amyloid disease onset, is particularly mysterious. Dye-binding fluorescence microscopy experiments have been used to observe the spontaneous formation fibril formation in microfluidic chambers from individual primary nucleation sites. These experiments revealed two key mechanistic details: 1) fibril formation propagated through solution as a traveling wave of constant velocity moving away from the primary nucleation site, and 2) there exists a linear relationship between the lag time before fibril formation and the inverse of volume. We hypothesize that the confinement of insulin to smaller volumes is an evolutionary adaptation that renders amyloid fibril formation prohibitively slow, in turn, influencing the size of insulin granules in pancreatic beta cells. We will develop novel top-down coarse-grained model that utilize a bridged approach, whereby two representations of an ensemble of fibril-forming proteins (one purely topological network representation and one granular representation in explicit space) exchange information as time evolves. This approach will leverage the high computational efficiency of exponential-family random graph models (purely topological), with improved spatial realism provided by a minimal explicit space model based on a Lennard-Jones fluid. The models will first be fit using a threefold validation strategy whereby they will be parameterized to simultaneously reproduce three known experimental observables: the fibril’s topological structure (derived from structures reported in the protein data bank), fibril growth kinetics (compared to dye- binding fluorescence experiments), and the spatial propagation patterns of fibril formation (compared to aforementioned microfluidic experiments). Analysis of the validated models will then be used to propose potential mechanisms for primary nucleation, the modulation of which is actively being explored for the development of preventative treatments for amyloid diseases. The proposed work will require an innovation to the network Hamiltonian methodology (first introduced by the PI and others), in that it will be the first to include explicit spatial degrees of freedom. This development will facilitate the comparison of network Hamiltonian models to experimental results and enhance the predictive power of the simulations, for both the present work and future studies in molecular self-assembly.