Computational Nanomedicine: Simulating Protein Misfolding Disease

Protein misfolding diseases such as Alzheimer's Disease (AD) and Huntington's Disease (HD) are challenging to study experimentally. Thus, computational methods, if they are able to be sufficiently accurate and reach sufficiently long timescales, can naturall contribute in this challenging area. I will discuss our recent results using novel methods within the Folding@home distributed computing project on progress towards a molecular understanding of protein aggregation involved in AD and HD. Specifically, I will detail our extensions to simulation Markov State Model methodology as well as specific predictions that arose from these simulations and experimental validation of these predictions. These results lead to a novel hypothesis for the structural basis of Abeta aggregation as well as the ability to explain existing experimental data.