Atomic force microscopy of ionotropic receptors bearing subunit-specific tags provides a method for determining receptor architecture
Bibliographic record
Abstract
We have developed an atomic force microscopy (AFM)-based method for the determination of the subunit architecture of ionotropic receptors, and tested the method using the GABA A receptor as a model system. The most common form of the GABA A receptor probably consists of 2α 1 -, 2β 2 - and 1γ 2 -subunits. We show here that the arrangement of subunits around the central Cl − ion channel can be deduced by AFM of receptors tagged with subunit-specific antibodies. Transfection of cells with DNA encoding α 1 -, β 2 - and γ 2 -subunits resulted in the production of receptors containing all three subunits, as judged by both immunoblot analysis and the binding of [ 3 H]-Ro15-1788, a specific radioligand for the GABA A receptor. A His 6 -tag on the α 1 -subunit was used to purify the receptor from membrane fractions of transfected cells. After incubation with anti-His 6 immunoglobulin G, some receptors became tagged with either one or two antibody molecules. AFM analysis of complexes containing two bound antibodies showed that the most common angle between the two tags was 135°, close to the value of 144° expected if the two α-subunits are separated by a third subunit. This method is applicable to the complete elucidation of the subunit arrangement around the GABA A receptor rosette, and can also be applied to other ionotropic receptors.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".