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Record W2996587712 · doi:10.1021/acsami.9b19472

Labeling of Proteins by BODIPY-Quinone Methides Utilizing Anti-Kasha Photochemistry

2019· article· en· W2996587712 on OpenAlexafffund
Katarina Zlatić, Ivana Antol, Lidija Uzelac, Ana-Matea Mikecin Dražić, Marijeta Kralj, Cornelia Bohne, Nikola Basarić

Bibliographic record

VenueACS Applied Materials & Interfaces · 2019
Typearticle
Languageen
FieldMaterials Science
TopicLuminescence and Fluorescent Materials
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaHrvatska Zaklada za Znanost
KeywordsBODIPYPhotochemistryFluorophoreFluorescenceFlash photolysisAdductChemistryOrganic chemistryKinetics

Abstract

fetched live from OpenAlex

A novel approach for the photolabeling of proteins by a BODIPY fluorophore is reported that is based on an anti-Kasha photochemical reaction from an upper singlet excited state (Sn) leading to the deamination of the BODIPY quinone methide precursor. On the other hand, the high photochemical stability of the dye upon excitation by visible light to S1 allows for the selective fluorescence detection from the dye or dye–protein adduct, without concomitant bleaching or hydrolysis of the protein-dye adduct. Therefore, photolabeling and fluorescence monitoring can be uncoupled by using different excitation wavelengths. Combined theoretical and experimental studies by preparative irradiations, fluorescence, and laser flash photolysis fully disclose the photophysical properties of the dye and its anti-Kasha photochemical reactivity. The application of the dye was demonstrated on photolabeling of bovine serum albumin.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.003
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.010
GPT teacher head0.237
Teacher spread0.227 · 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; both teacher heads agree on what is shown here.

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

Citations32
Published2019
Admission routes2
Has abstractyes

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