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Manipulating the aggregation activity of human prion-like proteins
Sean M Cascarina1, Kacy R Paul1, Eric D Ross1
1a Department of Biochemistry and Molecular Biology , Colorado State University , Fort Collins , CO , USA.
Prion
|September 22, 2017
Summary
The yeast prion prediction algorithm PAPA effectively predicts how mutations affect protein aggregation. While accurate for intrinsic aggregation, it doesn't fully capture in vivo cellular influences on prion-like activity.
Area of Science:
- Biochemistry
- Molecular Biology
- Neuroscience
Background:
- Yeast prion prediction algorithms have advanced due to understanding protein features.
- Mutations in human hnRNPA2B1 cause multisystem proteinopathy and protein aggregation.
- hnRNPA2B1 aggregation is accelerated in vitro and forms inclusions in vivo.
Purpose of the Study:
- To evaluate the yeast prion prediction algorithm PAPA for designing mutations in hnRNPA2B1.
- To assess PAPA's utility in modulating aggregation activity of hnRNPA2B1.
- To understand the role of intracellular factors in prion-like activity.
Main Methods:
- Systematic exploration of the PAPA algorithm's utility.
- Designing mutations in the prion-like domain (PrLD) of hnRNPA2B1.
- Expressing mutant hnRNPA2B1 in Drosophila and yeast models.
- Assessing aggregation propensity in vitro and prion activity in vivo.
Main Results:
- PAPA accurately predicted mutation effects on yeast prion activity and in vitro aggregation.
- PAPA predicted most, but not all, mutation effects on hnRNPA2B1 in Drosophila.
- Intrinsic aggregation propensity is well-predicted, but intracellular factors influence in vivo activity.
Conclusions:
- PAPA is effective for predicting intrinsic protein aggregation propensity.
- Intracellular factors significantly influence prion-like activity in vivo.
- Further research into intracellular factors is needed for next-generation prediction algorithms.