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Record W2092570578 · doi:10.1002/cbic.200700481

Specificity Fingerprinting of Retaining β‐1,4‐Glycanases in the <i>Cellulomonas fimi</i> Secretome Using Two Fluorescent Mechanism‐Based Probes

2007· article· en· W2092570578 on OpenAlexafffund
Omid Hekmat, Christine Florizone, Young‐Wan Kim, Lindsay D. Eltis, R. Antony J. Warren, Stephen G. Withers

Bibliographic record

VenueChemBioChem · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGlycosylation and Glycoproteins Research
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsChemistryCellulaseFluorescenceEnzymeBiochemistryProteomicsMass spectrometryGlycoside hydrolaseFluorescent labellingBiophysicsChromatographyBiologyGene

Abstract

fetched live from OpenAlex

Functional proteomics methods are crucial for activity- and mechanism-based investigation of enzymes in biological systems at a post-translational stage. Glycosidases have central roles in cellular metabolism and its regulation, and their dysfunction can have detrimental effects. These enzymes also play key roles in biomass conversion. A functional profiling methodology was developed for direct, fluorescence-based, in-gel analysis of retaining beta-glycosidases. Two spectrally nonoverlapping fluorescent, mechanism-based probes containing different recognition elements for retaining cellulases and xylanases were prepared. The specificity-based covalent labelling of retaining glycanases by the two probes was demonstrated in model enzyme mixtures. Using the two probes and mass spectrometry, the secretomes of the biomass-converting bacterium Cellulomonas fimi, under induction by different polyglycan growth substrates, were analysed to obtain a specificity profile of the C. fimi retaining beta-glycanases. This is a facile strategy for the analysis of glycosidases produced by biomass-degrading organisms.

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 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 score0.005
Threshold uncertainty score0.649

Codex and Gemma teacher scores by category

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

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 head0.030
GPT teacher head0.310
Teacher spread0.280 · 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 teacher head, 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

Citations19
Published2007
Admission routes2
Has abstractyes

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