Tyrosine 62 of the γ-Aminobutyric Acid Type A Receptor β2 Subunit Is an Important Determinant of High Affinity Agonist Binding
Bibliographic record
Abstract
The gamma-aminobutyric acid type A receptor (GABA(A)R) carries both high (K(D) = 10-30 nm) and low (K(D) = 0.1-1.0 microm) affinity binding sites for agonists. We have used site-directed mutagenesis to identify a specific residue in the rat beta2 subunit that is involved in high affinity agonist binding. Tyrosine residues at positions 62 and 74 were mutated to either phenylalanine or serine and the effects on ligand binding and ion channel activation were investigated after the expression of mutant subunits with wild-type alpha1 and gamma2 subunits in tsA201 cells or in Xenopus oocytes. None of the mutations affected [(3)H]Ro15-4513 binding or impaired allosteric interactions between the low affinity GABA and benzodiazepine sites. Although mutations at position 74 had little effect on [(3)H]muscimol binding, the Y62F mutation decreased the affinity of the high affinity [(3)H]muscimol binding sites by approximately 6-fold, and the Y62S mutation led to a loss of detectable high affinity binding sites. After expression in oocytes, the EC(50) values for both muscimol and GABA-induced activation of Y62F and Y62S receptors were increased by 2- and 6-fold compared with the wild-type. We conclude that Tyr-62 of the beta subunit is an important determinant for high affinity agonist binding to the GABA(A) receptor.
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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".