Allosteric Regulation and Spatial Distribution of Kainate Receptors Bound to Ancillary Proteins
Bibliographic record
Abstract
A diverse range of accessory proteins regulates the behaviour of most ligand- and voltage-gated ion channels. For glutamate receptor 6 (GluR6) kainate receptors, two unrelated proteins, concanavalin-A (Con-A) and postsynaptic density protein 95 (PSD-95), bind to extra- and intracellular domains, respectively, but are reported to exert similar effects on GluR6 desensitization behaviour. We have tested the hypothesis that distinct allosteric binding sites control GluR6 receptors via a common transduction pathway. Rapid agonist application to excised patches revealed that neither Con-A nor PSD-95 affect the onset of desensitization. The rate of desensitization elicited by 10 mM L-glutamate was similar in control (taufast = 5.5 +/- 0.4 ms), Con-A-treated patches (taufast = 6.1 +/- 0.5 ms) and patches containing PSD-95 and GluR6 receptors (taufast = 4.7 +/- 0.6 ms). Likewise, the time course of recovery from GluR6 desensitization was similar in both control and Con-A conditions, whereas PSD-95 accelerated recovery almost twofold. Peak and steady-state (SS) dose-response relationships to glutamate were unchanged by lectin treatment (e.g. control, EC50(SS) = 31 +/- 28 microM vs Con-A, EC50(SS) = 45 +/- 9 microM, n = 6), suggesting that Con-A does not convert non-conducting channels with high agonist affinity into an open conformation. Instead, we demonstrate that the effects of Con-A on macroscopic responses reflect a shift in the relative contribution of different open states of the channel. In contrast, the effect of PSD-95 on recovery behaviour suggests that the association between kainate receptors and cytoskeletal proteins regulates signalling at glutamatergic synapses. Our results show that Con-A and PSD-95 regulate kainate receptors via distinct allosteric mechanisms targeting selective molecular steps in the transduction pathway.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".