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Rupturing the hemi-fission intermediate in membrane fission under tension: Reaction coordinates, kinetic pathways, and free-energy barriers.

Guojie ZhangMarcus Müller
Published in: The Journal of chemical physics (2018)
Membrane fission is a fundamental process in cells, involved inter alia in endocytosis, intracellular trafficking, and virus infection. Its underlying molecular mechanism, however, is only incompletely understood. Recently, experiments and computer simulation studies have revealed that dynamin-mediated membrane fission is a two-step process that proceeds via a metastable hemi-fission intermediate (or wormlike micelle) formed by dynamin's constriction. Importantly, this hemi-fission intermediate is remarkably metastable, i.e., its subsequent rupture that completes the fission process does not occur spontaneously but requires additional, external effects, e.g., dynamin's (unknown) conformational changes or membrane tension. Using simulations of a coarse-grained, implicit-solvent model of lipid membranes, we investigate the molecular mechanism of rupturing the hemi-fission intermediate, such as its pathway, the concomitant transition states, and barriers, as well as the role of membrane tension. The membrane tension is controlled by the chemical potential of the lipids, and the free-energy landscape as a function of two reaction coordinates is obtained by grand canonical Wang-Landau sampling. Our results show that, in the course of rupturing, the hemi-fission intermediate undergoes a "thinning → local pinching → rupture/fission" pathway, with a bottle-neck-shaped cylindrical micelle as a transition state. Although an increase of membrane tension facilitates the fission process by reducing the corresponding free-energy barrier, for biologically relevant tensions, the free-energy barriers still significantly exceed the thermal energy scale kBT.
Keyphrases
  • molecular dynamics
  • single cell
  • induced apoptosis
  • oxidative stress
  • climate change
  • endoplasmic reticulum stress
  • neuropathic pain
  • deep learning
  • reactive oxygen species