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How do intrinsically disordered protein regions encode a driving force for liquid–liquid phase separation?

Open access
Borcherds, Wade, Bremer, Anne, Borgia, Madeleine B, Mittag, Tanja·2021·Current Opinion in Structural Biology

Abstract

Liquid-liquid phase separation is the mechanism underlying the formation of biomolecular condensates. Disordered protein regions often drive phase separation, but molecular interactions of disordered protein regions are not well understood, sometimes leading to the conflation that all disordered protein regions drive phase separation. Given the critical role of phase separation in many cellular processes, and that dysfunction of phase separation can lead to debilitating diseases, it is important that we understand the interactions and sequence properties underlying phase behavior. A conceptual framework that divides IDRs into interacting and solvating regions has proven particularly useful, and analytical instantiations and coarse-grained models can test our understanding of the driving forces against experimental phase behavior. Validated simulation paradigms enable the exploration of sequence space to help our understanding of how disordered protein regions can encode phase behavior, which IDRs may mediate phase separation in cells, and which IDRs are in contrast highly soluble.

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Metadata

Authors
Borcherds, W., Bremer, A., Borgia, M. B., & Mittag, T.
Venue
Current Opinion in Structural Biology
Publisher
Elsevier BV
Published
2021-04
Volume
67
Pages
41-50
ISSN
0959-440X (ISSN-L)
OpenAlex
W3047984729
Cited by
367
References
6
FWCI
2.64
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: How do intrinsically disordered protein regions encode a driving force for liquid–liquid phase separation? https://app.cochat.ai/papers/10.1016/j.sbi.2020.09.004
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Borcherds, W., Bremer, A., Borgia, M. B., & Mittag, T. (2021). How do intrinsically disordered protein regions encode a driving force for liquid–liquid phase separation? Current Opinion in Structural Biology. https://doi.org/10.1016/j.sbi.2020.09.004

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

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