Protein interaction quantified <i>in vivo</i> by spectrally resolved fluorescence resonance energy transfer
Bibliographic record
Abstract
We describe a fluorescence resonance energy transfer (FRET)-based method for finding in living cells the fraction of a protein population (alpha(T)) forming complexes, and the average number (n) of those protein molecules in each complex. The method relies both on sensitized acceptor emission and on donor de-quenching (by photobleaching of the acceptor molecules), coupled with full spectral analysis of the differential fluorescence signature, in order to quantify the donor/acceptor energy transfer. The approach and sensitivity limits are well suited for in vivo microscopic investigations. This is demonstrated using a scanning laser confocal microscope to study complex formation of the sterile 2 alpha-factor receptor protein (Ste2p), labelled with green, cyan, and yellow fluorescent proteins (GFP, CFP, and YFP respectively), in budding yeast Saccharomyces cerevisiae. A theoretical model is presented that relates the efficiency of energy transfer in protein populations (the apparent FRET efficiency, E(app)) to the energy transferred in a single donor/acceptor pair (E, the true FRET efficiency). We determined E by using a new method that relies on E(app) measurements for two donor/acceptor pairs, Ste2p-CFP/Ste2p-YFP and Ste2p-GFP/Ste2p-YFP. From E(app) and E we determined alpha(T) approximately 1 and n approximately 2 for Ste2 proteins. Since the Ste2p complexes are formed in the absence of the ligand in our experiments, we conclude that the alpha-factor pheromone is not necessary for dimerization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".