Synthesis and Testing of Mechanism‐Based Protein‐Profiling Probes for Retaining Endo‐glycosidases
Bibliographic record
Abstract
New functional proteomics methods are required for targeting and identification of subsets of a proteome in an activity-based fashion. Glycosidases play critical roles in biology, yet a robust method for functional analysis of their activities and identities in biological proteomes is still lacking. An aryl 2-deoxy-2-fluoro xylobioside inactivator was conjugated through cleavable and noncleavable linker arms to a biotin tag, thereby yielding two new active-site-directed reagents for activity-based profiling of retaining beta-glycanases in complex proteomes. Crucially, these tagged reagents possess high specificity for their target enzymes with kinetic parameters similar to those of the untagged reagent. Western blotting showed that these reagents bind and covalently label active retaining beta-glycanases both in pure enzyme samples and in the secreted proteome of the soil bacterium Cellulomonas fimi. Such reagents therefore show great promise for future activity-based targeting of glycanases.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".