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Record W2165067067 · doi:10.1039/c0ob00455c

Dramatic increase of quench efficiency in “spacerless” dimaleimide fluorogens

2010· article· en· W2165067067 on OpenAlexafffund
Karine Caron, Virginie Lachapelle, Jeffrey W. Keillor

Bibliographic record

VenueOrganic & Biomolecular Chemistry · 2010
Typearticle
Languageen
FieldChemistry
TopicClick Chemistry and Applications
Canadian institutionsUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsMaleimideFluorophoreChemistryIntramolecular forceFluorescenceLabellingMoietyThiolReagentCombinatorial chemistryQuenching (fluorescence)StereochemistryOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.004
GPT teacher head0.221
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations30
Published2010
Admission routes2
Has abstractyes

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