bioRxivBiophysicsPreprint
Quantum Tunneling-Gated Vesicle Fusion: Proton-Coupled Electron Transfer and Mechanical Barrier Softening Shape Neurotransmitter Release Latency
Abstract
Neurotransmitter release in neurons requires synaptic vesicles to fuse rapidly with the presynaptic membrane after calcium entry, yet single-vesicle recordings show highly heterogeneous latency distributions with both fast events and long heavy tails. Most existing models fit these data empirically without mechanistic grounding. We introduce a quantum- mechanochemical model in which an initial proton-coupled electron transfer (PCET) step, governed by quantum tunneling, primes the vesicle for fusion, while a subsequent time-dependent mechanical barrier softening drives the final membrane merger. The scheme consists of three states: a closed SNARE complex that activates through PCET [Formula], a primed intermediate (P) that undergoes mechanical gating with an aging rate and forward rate k2(t), and a reversible slip-back process (k-1(t)) that sustains long-latency events. The PCET step is described by a Marcus-type tunneling expression with isotope-dependent mass terms, enabling direct prediction of the kinetic isotope effect (KIE) between protiated and deuterated conditions. Structural heterogeneity is included via a distribution of donor-acceptor distances. By calibrating the mechanochemical attempt frequency{gamma} to reproduce the typical early fusion probability (P [0- 5 ms]H{approx} 0.20), the model generates latency probability density functions (PDFs), cumulative distributions (CDFs), and hazard rates consistent with experimental observations. Parameter sweeps show how tunneling decay ({beta}tun) controls the KIE magnitude, while mechanical aging and back reaction redistribute early versus late events. This quantum-mechanical and force-activated framework provides a physically interpretable, testable alternative to purely empirical fits for single-vesicle fusion latency in neurons.
- organism: human
- assay: electrophysiology
- research focus: cell signaling & trafficking
- approach: deep learning
- approach: statistical model
- Citations
- 0
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