Aggregation and degradation scales for prion-like domains: sequence features and context weigh in

Generalizable Compositional Features Influencing the Proteostatic Fates of Polar Low-Complexity Domains

Protein aggregation is associated with a growing list of human diseases. A substantial fraction of proteins in eukaryotic proteomes constitutes a proteostasis network—a collection of proteins that work together to maintain properly folded proteins. One of the overarching functions of the proteostasis network is the prevention or reversal of protein aggregation. How proteins aggregate in spite of the anti-aggregation activity of the proteostasis machinery is incompletely understood. Exposed hydrophobic patches can trigger degradation by the ubiquitin-proteasome system, a key branch of the proteostasis network. However, in a recent study, we found that model glycine (G)-rich or glutamine/asparagine (Q/N)-rich prion-like domains differ in their susceptibility to detection and degradation by this system. Here, we expand upon this work by examining whether the features controlling the degradation of our model prion-like domains generalize broadly to G-rich and Q/N-rich domains. Experimentally, native yeast G-rich domains in isolation are sensitive to the degradation-promoting effects of hydrophobic residues, whereas native Q/N-rich domains completely resist these effects and tend to aggregate instead. Bioinformatic analyses indicate that native G-rich domains from yeast and humans tend to avoid degradation-promoting features, suggesting that the proteostasis network may act as a form of selection at the molecular level that constrains the sequence space accessible to G-rich domains. However, the sensitivity or resistance of G-rich and Q/N-rich domains, respectively, was not always preserved in their native protein contexts, highlighting that proteins can evolve other sequence features to overcome the intrinsic sensitivity of some LCDs to degradation.

Composition-based prediction and rational manipulation of prion-like domain recruitment to stress granules

Mutations in a number of stress granule-associated proteins have been linked to various neurodegenerative diseases. Several of these mutations are found in aggregation-prone prion-like domains (PrLDs) within these proteins. In this work, we examine the sequence features governing PrLD localization to stress granules upon stress. We demonstrate that many yeast PrLDs are sufficient for stress-induced assembly into microscopically visible foci that colocalize with stress granule markers. Additionally, compositional biases exist among PrLDs that assemble upon stress, and these biases are consistent across different stressors. Using these biases, we have developed a composition-based prediction method that accurately predicts PrLD assembly into foci upon heat shock. We show that compositional changes alter PrLD assembly behavior in a predictable manner, while scrambling primary sequence has little effect on PrLD assembly and recruitment to stress granules. Furthermore, we were able to design synthetic PrLDs that were efficiently recruited to stress granules, and found that aromatic amino acids, which have previously been linked to PrLD phase separation, were dispensable for this recruitment. These results highlight the flexible sequence requirements for stress granule recruitment and suggest that PrLD localization to stress granules is driven primarily by amino acid composition, rather than primary sequence.