A Mechanism-Based ICAT Strategy for Comparing Relative Expression and Activity Levels of Glycosidases in Biological Systems
Bibliographic record
Abstract
An activity-based isotope-coded affinity tagging (AB-ICAT) strategy for proteome-wide quantitation of active retaining endoglycosidases has been developed. Two pairs of biotinylated, cleavable, AB-ICAT reagents (light H(8) and heavy D(8)) have been synthesized, one incorporating a recognition element for cellulases and the other incorporating a recognition element for xylanases. The accuracy of the AB-ICAT methodology in quantifying relative glycosidase expression/activity levels in any two samples of interest has been verified using several pairs of model enzyme mixtures where one or more enzyme amounts and/or activities were varied. The methodology has been applied to the biomass-degrading secretomes of the soil bacterium, Cellulomonas fimi, under induction by different polyglycan growth substrates to obtain a quantitative profile of the relative expression/activity levels of individual active retaining endoglycanases per C. fimi cell. Such biological profiles are valuable in understanding the strategies employed by biomass-degrading organisms in exploiting environments containing different biomass polysaccharides. This is the first report on the application of an activity-based ICAT method to a biological system.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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".