Dramatic increase of quench efficiency in “spacerless” dimaleimide fluorogens
Bibliographic record
Abstract
In this post-genomic era, new techniques are needed to cope with the task of assigning functional roles to the huge number of identified putative gene products. We have developed a minimalist labelling strategy based on the use of synthetic fluorogenic probe reagents that fluoresce only after their reaction with a target peptide sequence. The probe reagents have fluorescent cores and bear two maleimide groups, such that their latent fluorescence is quenched by a photoinduced electron transfer (PET) to the pendant maleimide groups, until both of these groups undergo a specific thiol addition reaction. The efficiency of the fluorescence quenching is critical to the practicality of this labelling method, and has been predicted to be related to the intramolecular distance between the fluorophore and the maleimide groups. We have conducted the first direct test of this hypothesis by preparing a series of novel fluorogens that differ only by the spacer moiety separating their coumarin fluorophore and their dimaleimide fragment. A striking correlation was observed between intramolecular distance and the fluorescence enhancement (FE) observed after reaction with two equivalents of thiol. Guided by this observation, we then designed 'spacerless' fluorogens, of which a dansyl derivative shows an FE ratio of >300, the largest recorded for dimaleimide fluorogens. The trends observed herein provide valuable lessons for subsequent fluorogen design, and the novel fluorogens developed in the course of this study are currently being applied to protein labelling applications.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 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".