Burkholderia cenocepacia and Salmonella enterica ArnT proteins that transfer 4-amino-4-deoxy-l-arabinose to lipopolysaccharide share membrane topology and functional amino acids
Bibliographic record
Abstract
We recently demonstrated that incorporation of 4-amino-4-deoxy-l-arabinose (l-Ara4N) to the lipid A moiety of lipopolysaccharide (LPS) is required for transport of LPS to the outer membrane and viability of the Gram-negative bacterium Burkholderia cenocepacia. ArnT is a membrane protein catalyzing the transfer of l-Ara4N to the LPS molecule at the periplasmic face of the inner membrane, but its topology and mechanism of action are not well characterized. Here, we elucidate the topology of ArnT and identify key amino acids that likely contribute to its enzymatic function. PEGylation assays using a cysteineless version of ArnT support a model of 13 transmembrane helices and a large C-terminal region exposed to the periplasm. The same topological configuration is proposed for the Salmonella enterica serovar Typhimurium ArnT. Four highly conserved periplasmic residues in B. cenocepacia ArnT, tyrosine-43, lysine-69, arginine-254 and glutamic acid-493, were required for activity. Tyrosine-43 and lysine-69 span two highly conserved motifs, (42)RYA(44) and (66)YFEKP(70), that are found in ArnT homologues from other species. The same residues in S. enterica ArnT are also needed for function. We propose these aromatic and charged amino acids participate in either undecaprenyl phosphate-l-Ara4N substrate recognition or transfer of l-Ara4N to the LPS.
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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".