Brighter Red Fluorescent Proteins by Rational Design of Triple-Decker Motif
Bibliographic record
Abstract
Red fluorescent proteins (RFPs) are used extensively in chemical biology research as fluorophores for live cell imaging, as partners in FRET pairs, and as signal transducers in biosensors. For all of these applications, brighter RFP variants are desired. Here, we used rational design to increase the quantum yield of monomeric RFPs in order to improve their brightness. We postulated that we could increase quantum yield by restricting the conformational degrees of freedom of the RFP chromophore. To test our hypothesis, we introduced aromatic residues above the chromophore of mRojoA, a dim RFP containing a π-stacked Tyr residue directly beneath the chromophore, in order to reduce chromophore conformational flexibility via improved packing and steric complementarity. The best mutant identified displayed an absolute quantum yield increase of 0.07, representing an over 3-fold improvement relative to mRojoA. Remarkably, this variant was isolated following the screening of only 48 mutants, a library size that is several orders of magnitude smaller than those previously used to achieve equivalent gains in quantum yield in other RFPs. The crystal structure of the highest quantum yield mutant showed that the chromophore is sandwiched between two Tyr residues in a triple-decker motif of aromatic rings. Presence of this motif increases chromophore rigidity, as evidenced by the significantly reduced temperature factors compared to dim RFPs. Overall, the approach presented here paves the way for the rapid development of fluorescent proteins with higher quantum yield and overall brightness.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".