Membrane-gated SNARE zippering focuses energy for fusion
Kumar, A.; Yang, J.; Anmolsingh, L.; Eitel, A. R.; Xi, Z.; Lin, L.; Hamm, H. E.; Zhang, Y.
Show abstract
Soluble N-ethylmaleimide-sensitive factor attachment protein receptors (SNAREs) drive stagewise membrane fusion by zippering into membrane-bridging four-helix bundles. Yet the conformations underlying successive fusion stages and the coupling of folding energy to bilayer remodeling remain unclear. Using optical tweezers, we measured the intermediates, energetics, kinetics, and force dependence of individual synaptic SNARE complexes assembled in cis on single membranes and in trans between apposed membranes. Membrane-anchored cis-SNAREs assembled through N-terminal and cooperative C-terminal/linker-domain transitions, whereas their transmembrane domains showed little intrinsic dimerization. Syntaxin retained membrane-dependent helical continuity through its linker domain before zippering was complete. PIP2 strengthened but slowed late zippering. In trans, membrane repulsion arrested single trans-SNARE complexes in a half-zippered state. G{beta}{gamma} further clamped this intermediate and inhibited late zippering; G-GDP, but not G-GTP{gamma}S, relieved the clamp, revealing a nucleotide-dependent mechanism for GPCR-mediated inhibition of neurotransmitter release. Modeling suggests that cooperative late zippering, syntaxin linker helicity, and concerted action of multiple SNAREs focus folding energy released over a long distance onto short-range membrane apposition. Thus, mechanically gated SNARE zippering is regulated by membrane forces, lipids, and regulatory proteins.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.