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Cited by 381

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Thermodynamics and Mechanics of Membrane Curvature Generation and Sensing by Proteins and Lipids

Open access
Baumgart, Tobias, Capraro, Benjamin R., Zhu, Chen, Das, Sovan L.·2011·Annual Review of Physical Chemistry

Abstract

Research investigating lipid membrane curvature generation and sensing is a rapidly developing frontier in membrane physical chemistry and biophysics. The fast recent progress is based on the discovery of a plethora of proteins involved in coupling membrane shape to cellular membrane function, the design of new quantitative experimental techniques to study aspects of membrane curvature, and the development of analytical theories and simulation techniques that allow a mechanistic interpretation of quantitative measurements. The present review first provides an overview of important classes of membrane proteins for which function is coupled to membrane curvature. We then survey several mechanisms that are assumed to underlie membrane curvature sensing and generation. Finally, we discuss relatively simple thermodynamic/mechanical models that allow quantitative interpretation of experimental observations.

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Crossref · 10.1146/annurev.physchem.012809.103450

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Metadata

Authors
Baumgart, T., Capraro, B. R., Zhu, C., & Das, S. L.
Venue
Annual Review of Physical Chemistry
Published
2011-05-05
Volume
62
Issue
1
Pages
483-506
ISSN
  • 0066-426X (ISSN-L)
  • 1545-1593
OpenAlex
W2119253842
Cited by
381
References
4
FWCI
1.80
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Thermodynamics and Mechanics of Membrane Curvature Generation and Sensing by Proteins and Lipids. https://app.cochat.ai/papers/10.1146/annurev.physchem.012809.103450
The paper
Baumgart, T., Capraro, B. R., Zhu, C., & Das, S. L. (2011). Thermodynamics and Mechanics of Membrane Curvature Generation and Sensing by Proteins and Lipids. Annual Review of Physical Chemistry. https://doi.org/10.1146/annurev.physchem.012809.103450

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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.