Modeling the self-assembly dynamics of macromolecular protein aggregates underlying neurodegenerative disorders

Many of the neurodegenerative disorders associated with aging, for example Alzheimer's disease, are thought to be associated with the large-scale self-assembly of nanoscale protein aggregates in the brain. A better understanding of this aggregation process might hold the key to improved therapies and even prevention. However, the individual peptide structures are so complex that molecular dynamics (MD) simulations of the entire aggregation process are impossible. Here we outline a novel approach to this many-protein problem, in which the goal is to extract the key interaction characteristics from large-scale MD simulations of few-peptide interactions, and then use these as the input to rule-based (i.e., agent-based) aggregation models. We investigate and discuss the likely consequences of such a procedure by examining a range of feasible aggregation processes.