Understanding amyloid fibril growth through theory and simulation - Beugelsdijk, Alex (Kansas State University, 2014)
Proteins are fundamental building blocks of life in an organism, and to function properly, they must adopt an appropriate three-dimensional conformation or conformational ensemble. In protein aggregation diseases, proteins misfold to incorrect structures that allow them to join together and form aggregates. A wide variety of proteins are involved in these aggregation diseases and there are multiple theories of their disease mechanism. However, a common theme is that they aggregate into filamentous structures. Therapies that target the process by which the aggregating proteins assemble into these similar fibril-like structures may by effective at countering aggregation diseases. This requires models that can accurately describe the assembly process of the fibrils. An analytical theory was recently described where fibrils grow by the templating of peptides onto an existing amyloid core and the kinetics of the templating process is modeled as a random walk in the backbone hydrogen bonding space. In this thesis, I present my work integrating molecular simulation with this analytical model to investigate the dependence of fibril growth kinetics on peptide sequence and other molecular details. Using the Aβ16-22 peptide as a model system, we first calculate the rate matrix of transitions among all possible hydrogen bonding microscopic states using numerous short-time simulations. These rates were then used to construct a kinetic Monte Carlo model for simulations of long-timescale fibril growth. The results demonstrate the feasibility of using such a theory/simulation framework for bridging the significant gap between fibril growth and simulation timescales. At the same time, the study also reveals some limits of describing the fibril growth as a templating process in the backbone hydrogen bonding space alone. In particular, we found that dynamics in nonspecifically bound states must also be considered. Possible solutions to this deficiency are discussed at the end.
Keywords: Alzheimer's Disease; Amyloid; Aggregation; Protein; Simulation; Molecular Dynamics; Biochemistry (0487); Biophysics (0786)