A previously uncharacterized O-glycopeptidase from Akkermansia muciniphila requires the Tn-antigen for cleavage of the peptide bond
Bibliographic record
Abstract
Akkermansia muciniphila is key member of the human gut microbiota that impacts many features of host health. A major characteristic of this bacterium is its interaction with host mucin, which is abundant in the gut environment, and its ability to metabolize mucin as a nutrient source. The machinery deployed by A. muciniphila to enable this interaction appears to be extensive and sophisticated, yet it is incompletely defined. The uncharacterized protein AMUC_1438 is encoded by a gene that was previously shown to be upregulated when the bacterium is grown on mucin. This uncharacterized protein has features suggestive of carbohydrate-recognition and peptidase activity, which led us to hypothesize that it has a role in mucin depolymerization. Here, we provide structural and functional support for the assignment of AMUC_1438 as a unique O-glycopeptidase with mucin-degrading capacity. O-glycopeptidase enzymes recognize glycans but hydrolyze the peptide backbone and are common in host-adapted microbes that colonize or invade mucus layers. Structural, kinetic, and mutagenic analyses point to a metzincin metalloprotease catalytic motif but with an active site that specifically recognizes a GalNAc residue α-linked to serine or threonine (i.e., the Tn-antigen). The enzyme catalyzes hydrolysis of the bond immediately N-terminal to the glycosylated residue. Additional modeling analyses suggest the presence of a carbohydrate-binding module that may assist in substrate recognition. We anticipate that these results will be fundamental to a wider understanding of the O-glycopeptidase class of enzymes and how they may contribute to host adaptation.
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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".