The effect of proximity on the function and energy transfer capability of fluorescent protein pairs
Bibliographic record
Abstract
Abstract Fluorescent proteins (FPs) are commonly used in pairs to monitor dynamic biomolecular events through changes in their proximity via distance dependent processes such as Förster resonance energy transfer (FRET). Many FPs have a tendency to oligomerise, which is likely to be promoted through attachment to associating proteins through increases in local FP concentration. We show here that on association of FP pairs, the inherent function of the FPs can alter. Artificial dimers were constructed using a bioorthogonal Click chemistry approach that combined a commonly used green fluorescent protein (superfolder GFP) with itself, a yellow FP (Venus) or a red FP (mCherry). In each case dimerisation changes the inherent fluorescent properties, including FRET capability. The GFP homodimer demonstrated synergistic behaviour with the dimer being brighter than the sum of the two monomers. The structure of the GFP homodimer revealed that a water-rich interface is formed between the two monomers, with the chromophores being in close proximity with favourable transition dipole alignments. Dimerisation of GFP with Venus results in a complex displaying ∼86% FRET efficiency, which is significantly below the near 100% efficiency predicted. When GFP is complexed with mCherry, FRET and mCherry fluorescence itself is essentially lost. Thus, the simple assumptions used when monitoring interactions between proteins via FP FRET may not always hold true, especially under conditions whereby the protein-protein interactions promote FP interaction. Abstract Figure
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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 | 0.001 |
| 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) | 0.003 | 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".