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Atomic-Resolution Simulations Predict a Transition State for Vesicle Fusion Defined by Contact of a Few Lipid Tails

Overview of attention for article published in PLoS Computational Biology, June 2010
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Title
Atomic-Resolution Simulations Predict a Transition State for Vesicle Fusion Defined by Contact of a Few Lipid Tails
Published in
PLoS Computational Biology, June 2010
DOI 10.1371/journal.pcbi.1000829
Pubmed ID
Authors

Peter M. Kasson, Erik Lindahl, Vijay S. Pande

Abstract

Membrane fusion is essential to both cellular vesicle trafficking and infection by enveloped viruses. While the fusion protein assemblies that catalyze fusion are readily identifiable, the specific activities of the proteins involved and nature of the membrane changes they induce remain unknown. Here, we use many atomic-resolution simulations of vesicle fusion to examine the molecular mechanisms for fusion in detail. We employ committor analysis for these million-atom vesicle fusion simulations to identify a transition state for fusion stalk formation. In our simulations, this transition state occurs when the bulk properties of each lipid bilayer remain in a lamellar state but a few hydrophobic tails bulge into the hydrophilic interface layer and make contact to nucleate a stalk. Additional simulations of influenza fusion peptides in lipid bilayers show that the peptides promote similar local protrusion of lipid tails. Comparing these two sets of simulations, we obtain a common set of structural changes between the transition state for stalk formation and the local environment of peptides known to catalyze fusion. Our results thus suggest that the specific molecular properties of individual lipids are highly important to vesicle fusion and yield an explicit structural model that could help explain the mechanism of catalysis by fusion proteins.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 103 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
Germany 2 2%
United Kingdom 2 2%
France 1 <1%
Argentina 1 <1%
Portugal 1 <1%
Unknown 93 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 35 34%
Student > Ph. D. Student 28 27%
Professor 7 7%
Student > Master 5 5%
Professor > Associate Professor 4 4%
Other 8 8%
Unknown 16 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 23 22%
Chemistry 20 19%
Biochemistry, Genetics and Molecular Biology 17 17%
Physics and Astronomy 10 10%
Materials Science 4 4%
Other 12 12%
Unknown 17 17%