Tau is a natively unfolded protein that has been implicated in the pathogenesis of several neurodegenerative disorders such as Alzheimer's disease (AD). A number of studies suggest that aggregates of tau contribute to neuronal death and dysfunction in AD patients. Therefore, obtaining information about structural features that enable tau to form aggregates is of immense importance. In this proposal we develop and test novel approaches for constructing ensembles that adequately represent the unfolded state of tau. Our objective is to establish a new paradigm for building ensembles that adequately model the accessible conformations of intrinsically disordered proteins like tau. Using these methods we identify structural features within the unfolded ensemble of tau that are associated with aggregation. The goals of this proposal are to: 1) Generate structural ensembles that adequately represent accessible conformations of tau using computational methods (e.g., molecular dynamics simulations). Experimental data, such as chemical shifts, are used to guide the generation of appropriate ensembles;2) Generate similar structural ensembles for tau mutants, and phosphorylated forms of tau, that are associated with increased aggregation. Potential aggregation-prone conformers are identified by comparing ensembles corresponding to both native and aberrant proteins. Once aggregation-prone conformers have been identified, we will estimate the relative ability of potential aggregation-prone conformers to self-associate. Our long term goal is to use the information obtained from these studies to design small molecules that prevent the self-association of aggregation-prone conformers.
A number of studies suggest that aggregates of tau protein contribute to neuronal death and dysfunction in patients with Alzheimer's disease. We propose to identify structural features that enable tau to form aggregates in solution. Our long term goal is to use this insight to design potential therapies that prevent the formation of tau aggregates.
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