Skip to content
Preprint · OA via OpenAlex

When proteostasis goes bad: Protein aggregation in the cell

Mona Radwan, Rebecca J. Wood, Xiaojing Sui, Danny M. Hatters

IUBMB Life · 2017 · ▲ 41 citations

Abstract

Protein aggregation is a hallmark of the major neurodegenerative diseases including Alzheimer's, Parkinson's, Huntington's and motor neuron and is a symptom of a breakdown in the management of proteome foldedness. Indeed, it is remarkable that under normal conditions cells can keep their proteome in a highly crowded and confined space without uncontrollable aggregation. Proteins pose a particular challenge relative to other classes of biomolecules because upon synthesis they must typically follow a complex folding pathway to reach their functional conformation (native state). Non-native conformations, including the unfolded nascent chain, are highly prone to aberrant interactions, leading to aggregation. Here we review recent advances in knowledge of proteostasis(definition), approaches to monitor proteostasis and the impact that protein aggregation has on biology. We also include discussion of the outstanding challenges. © 2017 IUBMB Life, 69(2):49-54, 2017.

◌ CITATION ONLY
Full text is not openly licensed for redistribution here. Read it at the source:

Read at source →

Provenance

Source
OpenAlex
DOI
10.1002/iub.1597
Canonical
link ↗
Fetched
2026-06-09 MST

Cite this

APA
Radwan, M., Wood, R.J., Sui, X., &amp; Hatters, D.M. (2017). When proteostasis goes bad: Protein aggregation in the cell. <em>IUBMB Life</em>. https://doi.org/10.1002/iub.1597
Vancouver
Radwan M, Wood RJ, Sui X, Hatters DM. When proteostasis goes bad: Protein aggregation in the cell. IUBMB Life. 2017. doi:10.1002/iub.1597.
BibTeX
@unpublished{mona2017Whenpr, title = {When proteostasis goes bad: Protein aggregation in the cell}, author = {Mona Radwan and Rebecca J. Wood and Xiaojing Sui and Danny M. Hatters}, journal = {IUBMB Life}, year = {2017}, doi = {10.1002/iub.1597}, }

Research neighborhood

References, citing works, and semantically nearest findings. Click a node to open it.

Related findings