Specificity Fingerprinting of Retaining β‐1,4‐Glycanases in the <i>Cellulomonas fimi</i> Secretome Using Two Fluorescent Mechanism‐Based Probes
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".