The determinants of aggregation and toxicity of prion-like domains

Colorado State University, Fort Collins CO

Investigators

Linked publications & trials

Abstract

DESCRIPTION (provided by applicant): Prions are infectious proteins that result from the structural conversion of proteins into a self-templating conformation. In yeast, a number of glutamine/asparagine-rich proteins have been shown to undergo prion conversions from a soluble form to an insoluble amyloid form. A variety of evidence suggests that these prions may act as epigenetic regulatory elements. Interestingly, numerous human proteins also contain prion-like domains (PrLDs) - domains with compositional similarity to the yeast prion-forming domains. Six of these have recently been linked to various age-related degenerative disorders, heightening the interest in PrLDs. More than 200 other human proteins contain PrLDs, suggesting that aggregation of these proteins may be involved in other diseases. However, despite the importance of these domains in human disease, the sequence basis for their aggregation and toxicity is still poorly understood. Preliminary attempts to define the sequence basis for aggregation of PrLDs recently resulted in the development of PAPA (Prion Aggregation Prediction Algorithm), the first algorithm demonstrated to be able to distinguish between proteins with and without prion-like activity. This proposal builds on these early successes, using a combination of yeast and Drosophila genetics, in vitro experiments, and bioinformatics to rigorously define how the amino acid sequence of PrLDs contributes to aggregation and toxicity. In Specific Aim 1, a newly developed assay will be employed to quantitatively determine the compositional requirements for aggregation and toxicity of PrLDs. In Specific Aim 2, a combination of in vitro assays and Drosophila experiments will be used to explore how the propensity to convert to oligomeric and amyloid species affects toxicity in vivo. Finally, in Specific Aim 3, sophisticated bioinformatics methods will be used to incorporate this information into improved prediction algorithms, allowing for the identification of candidate disease-associated or regulatory PrLDs.