Brevity of the Ca<sup>2+</sup> Microdomain and Active Zone Geometry Prevent Ca<sup>2+</sup>-Sensor Saturation for Neurotransmitter Release
Bibliographic record
Abstract
The brief time course of the calcium (Ca2+) channel opening combined with the molecular-level colocalization of Ca2+ channels and synaptic vesicles in presynaptic terminals predict sub-millisecond calcium concentration ([Ca2+]) transients of > or = 100 microM in the immediate vicinity of the vesicle. This [Ca2+] is much higher than some of the recent estimates for the equilibrium dissociation constant of the Ca2+ sensor(s) that control neurotransmitter release, suggesting release should be close to saturation, yet it is well known that release is highly sensitive to changes in Ca2+ influx. We show that due to the brevity of the Ca2+ influx the binding kinetics of the Ca2+ sensor rather than its equilibrium affinity determine receptor occupancy. For physiologically relevant Ca2+ currents and forward Ca2+ binding rates, the effective affinity of the Ca2+ sensor can be several-fold lower than the equilibrium affinity. Using simple models, we show redundant copies of the binding sites increase effective affinity of the Ca2+ sensor for release. Our results predict that different levels of expression of Ca2+ binding sites could account for apparent differences in Ca2+ sensor affinities between synapses. Using Monte Carlo simulations of Ca2+ dynamics with nanometer resolution, we demonstrate that these kinetic constraints combined with vesicles acting as diffusion barriers can prevent saturation of the Ca2+-sensor(s) for neurotransmitter release. We further show the random positioning of the Ca2+-sensor molecules around the vesicle can result in the emergence of two distinct populations of the vesicles with low and high release probability. These considerations allow experimental evidence for the Ca2+ channel-vesicle colocalization to be reconciled with a high equilibrium affinity for the Ca2+ sensor of the release machinery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".