The Structure- and Metal-dependent Activity of Escherichia coli PgaB Provides Insight into the Partial De-N-acetylation of Poly-β-1,6-N-acetyl-d-glucosamine
Bibliographic record
Abstract
Exopolysaccharides are required for the development and integrity of biofilms produced by a wide variety of bacteria. In Escherichia coli, partial de-N-acetylation of the exopolysaccharide poly-β-1,6-N-acetyl-D-glucosamine (PNAG) by the periplasmic protein PgaB is required for polysaccharide intercellular adhesin-dependent biofilm formation. To understand the molecular basis for PNAG de-N-acetylation, the structure of PgaB in complex with Ni(2+) and Fe(3+) have been determined to 1.9 and 2.1 Å resolution, respectively, and its activity on β-1,6-GlcNAc oligomers has been characterized. The structure of PgaB reveals two (β/α)(x) barrel domains: a metal-binding de-N-acetylase that is a member of the family 4 carbohydrate esterases (CE4s) and a domain structurally similar to glycoside hydrolases. PgaB displays de-N-acetylase activity on β-1,6-GlcNAc oligomers but not on the β-1,4-(GlcNAc)(4) oligomer chitotetraose and is the first CE4 member to exhibit this substrate specificity. De-N-acetylation occurs in a length-dependent manor, and specificity is observed for the position of de-N-acetylation. A key aspartic acid involved in de-N-acetylation, normally seen in other CE4s, is missing in PgaB, suggesting that the activity of PgaB is attenuated to maintain the low levels of de-N-acetylation of PNAG observed in vivo. The metal dependence of PgaB is different from most CE4s, because PgaB shows increased rates of de-N-acetylation with Co(2+) and Ni(2+) under aerobic conditions, and Co(2+), Ni(2+) and Fe(2+) under anaerobic conditions, but decreased activity with Zn(2+). The work presented herein will guide inhibitor design to combat biofilm formation by E. coli and potentially a wide range of medically relevant bacteria producing polysaccharide intercellular adhesin-dependent biofilms.
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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) | 0.001 | 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".