Active-site Peptide “Fingerprinting” of Glycosidases in Complex Mixtures by Mass Spectrometry
Bibliographic record
Abstract
New proteomics methods are required for targeting and identification of subsets of a proteome in an activity-based fashion. Here, we report the first gel-free, mass spectrometry-based strategy for mechanism-based profiling of retaining beta-endoglycosidases in complex proteomes. Using a biotinylated, cleavable 2-deoxy-2-fluoroxylobioside inactivator, we have isolated and identified the active-site peptides of target retaining beta-1,4-glycanases in systems of increasing complexity: pure enzymes, artificial proteomes, and the secreted proteome of the aerobic mesophilic soil bacterium Cellulomonas fimi. The active-site peptide of a new C. fimi beta-1,4-glycanase was identified in this manner, and the peptide sequence, which includes the catalytic nucleophile, is highly conserved among glycosidase family 10 members. The glycanase gene (GenBank accession number DQ146941) was cloned using inverse PCR techniques, and the protein was found to comprise a catalytic domain that shares approximately 70% sequence identity with those of xylanases from Streptomyces sp. and a family 2b carbohydrate-binding module. The new glycanase hydrolyzes natural and artificial xylo-configured substrates more efficiently than their cello-configured counterparts. It has a pH dependence very similar to that of known C. fimi retaining glycanases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".