Two-Photon Excitation Fluorescence Cross-Correlation Assay for Ligand−Receptor Binding: Cell Membrane Nanopatches Containing the Human μ-Opioid Receptor
Bibliographic record
Abstract
Current ligand-receptor binding assays for G-protein coupled receptors cannot directly measure the system's dissociation constant, Kd, without purification of the receptor protein. Accurately measured Kd's are essential in the development of a molecular level understanding of ligand-receptor interactions critical in rational drug design. Here we report the introduction of two-photon excitation fluorescence cross-correlation spectroscopy (TPE-FCCS) to the direct analysis of ligand-receptor interactions of the human micro opioid receptor (hMOR) for both agonists and antagonists. We have developed the use of fluorescently distinct, dye-labeled hMOR-containing cell membrane nanopatches ( approximately 100-nm radius) and ligands, respectively, for this assay. We show that the output from TPE-FCCS data sets can be converted to the conventional Hill format, which provides Kd and the number of active receptors per nanopatch. When ligands are labeled with quantum dots, this assay can detect binding with ligand concentrations in the subnanomolar regime. Interestingly, conjugation to a bulky quantum dot did not adversely affect the binding propensity of the hMOR pentapeptide ligand, Leu-enkephalin.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".