Back

Coupling of Ca2+-triggered unclamping and membrane fusion during neurotransmitter release

McDargh, Z. A.; Polley, A.; Zeng, J.; O'Shaughnessy, B. A.

2021-06-17 neuroscience
10.1101/2021.06.16.448753 bioRxiv
Show abstract

Neurotransmitter (NT) release is accomplished by a machinery that unclamps fusion in response to calcium and then fuses the synaptic vesicle and plasma membranes. These are often thought of as distinct tasks assigned to non-overlapping components. Vesicle release rates have a power law dependence on [Ca2+] with an exponent of 3-5, long taken to indicate that 3-5 Ca2+ ions bind the calcium sensor Synaptotagmin to trigger release. However, dependencies at low [Ca] are inconsistent with simple sequential binding to a single Ca2+ sensor followed by a final fusion step. Here we developed coarse-grained molecular dynamics simulations of the NT release machinery accounting for Synaptotagmin-mediated unclamping and SNARE-mediated fusion. Calcium-triggered unclamping and SNARE-mediated fusion emerged from simulations as contemporaneous, coupled processes. Increasing cytosolic [Ca2+], the instantaneous fusion rate increased as SNAREpins were progressively and reversibly released by dissociation of Synaptotagmin-SNAREpin complexes. Simulations reproduced the observed dependence of release rates on [Ca2+], but the power law was unrelated to the number of Ca2+ ions required. Action potential-evoked vesicle release probabilities depended on the number of transiently unclamped SNAREpins, explaining experimental dependencies of release probabilities on both unclamping and membrane-fusing machinery components. These results describe a highly cooperative NT release machinery with intrinsically inseparable unclamping and membrane-fusing functionalities.

Matching journals

The top 1 journal accounts for 50% of the predicted probability mass.

50% of probability mass above

"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.