Substrate recognition and cleavage by mucin degrading <i>O</i> -glycopeptidases from the gut microbe <i>Bacteroides caccae</i>
Bibliographic record
Abstract
O-glycopeptidases are enzymes that hydrolyze the peptide bonds in glycoproteins by a mechanism that involves specific recognition of O-linked glycans on the substrate. Bacteroides caccae, an accomplished mucin degrader, is a member of the human gut microbiota with sixteen genes encoding putative O-glycopeptidases in the peptidase_M60 family. At present, the diversity of substrate selectivity in O-glycopeptidases is not well-understood, nor is the rationale behind their expansion in bacteria such as B. caccae. Here, we reveal the activity and diversity of the peptidase_M60 O-glycopeptidases encoded in the B. caccae genome. At least thirteen of the sixteen peptidase_M60 encoding genes produce active mucinolytic enzymes. Targeted functional studies by a high-throughput FRET screen combined with detailed kinetic analyses reveal that five examples in an uncharacterized clade of peptidase_M60 proteins are specifically O-glycopeptidases with different substrate selectivities despite their relatively high degree of relatedness. Structural analyses of these enzymes, including bound complexes, reveal new insight into the molecular underpinnings of O-glycopeptidase diversity. This highlights the larger context of how varied the selectivity of peptidase_M60 O-glycopeptidases can be for the glycan moiety and/or the peptide portion of the substrates, and why mucin degraders like B. caccae diversify O-glycopeptidase substrate repertoires to potentially maximize breakdown of this extraordinarily complex polymer.
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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".