The mechanism of peptidoglycan O-acetylation in Gram-negative bacteria typifies bacterial MBOAT-SGNH acyltransferases
Bibliographic record
Abstract
Abstract Bacterial cell envelope polymers are commonly modified with acyl groups that provide fitness advantages. Many polymer acylation pathways involve pairs of membrane-bound O -acyltransferase (MBOAT) and SGNH family proteins. As an example, the MBOAT protein PatA and the SGNH protein PatB are required in Gram-negative bacteria for peptidoglycan O-acetylation. The mechanism for how MBOAT-SGNH transferases move acyl groups from acyl-CoA donors made in the cytoplasm to extracellular polymers is unclear. Using the peptidoglycan O -acetyltransferase proteins PatAB, we explore the mechanism of MBOAT-SGNH pairs. We find that the MBOAT protein PatA catalyzes auto-acetylation of an invariant Tyr residue in its conserved C-terminal hexapeptide motif. We also show that PatB can use a synthetic hexapeptide containing an acetylated tyrosine to donate an acetyl group to a peptidoglycan mimetic. Finally, we report the structure of PatB, finding that it has structural features that shape its activity as an O -acetyltransferase and distinguish it from other SGNH esterases and hydrolases. Taken together, our results support a model for peptidoglycan acylation in which a tyrosine-containing peptide at the MBOAT’s C-terminus shuttles an acyl group from the MBOAT active site to the SGNH active site, where it is transferred to peptidoglycan. This model likely applies to other systems containing MBOAT-SGNH pairs, such as those that O-acetylate alginate, cellulose, and secondary cell wall polysaccharides. The use of an acyl-tyrosine intermediate for MBOAT-SGNH acyl transfer is also shared with AT3-SGNH proteins, a second major group of acyltransferases that modify cell envelope polymers.
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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 | 0.001 |
| 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 | 0.001 |
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".