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A Systematic Survey Identifies Prions and Illuminates Sequence Features of Prionogenic Proteins

Open access
Alberti, Simon, Halfmann, Randal, King, Oliver, Kapila, Atul, Lindquist, Susan·2009·Cell

Abstract

SUMMARY Prions are proteins that convert between structurally and functionally distinct states, one or more of which is transmissible. In yeast, this ability allows them to act as non-Mendelian elements of phenotypic inheritance. To further our understanding of prion biology, we conducted a bioinformatic proteome-wide survey for prionogenic proteins in S. cerevisiae, followed by experimental investigations of 100 prion candidates. We found an unexpected amino acid bias in aggregation-prone candidates and discovered that 19 of these could also form prions. At least one of these prion proteins, Mot3, produces a bona fide prion in its natural context that increases population-level phenotypic heterogeneity. The self-perpetuating states of these proteins present a vast source of heritable phenotypic variation that increases the adaptability of yeast populations to diverse environments.

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Crossref · 10.1016/j.cell.2009.02.044

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Metadata

Authors
Alberti, S., Halfmann, R., King, O., Kapila, A., & Lindquist, S.
Venue
Cell
Publisher
Elsevier BV
Published
2009-04
Volume
137
Issue
1
Pages
146-158
ISSN
0092-8674 (ISSN-L)
OpenAlex
W2119651404
Cited by
968
References
3
FWCI
21.96
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: A Systematic Survey Identifies Prions and Illuminates Sequence Features of Prionogenic Proteins. https://app.cochat.ai/papers/10.1016/j.cell.2009.02.044
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Alberti, S., Halfmann, R., King, O., Kapila, A., & Lindquist, S. (2009). A Systematic Survey Identifies Prions and Illuminates Sequence Features of Prionogenic Proteins. Cell. https://doi.org/10.1016/j.cell.2009.02.044

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.