Enzymatic Adenylation of 2,3-Dihydroxybenzoate Is Enhanced by a Protein−Protein Interaction between <i>Escherichia coli</i> 2,3-Dihydro-2,3-dihydroxybenzoate Dehydrogenase (EntA) and 2,3-Dihydroxybenzoate-AMP Ligase (EntE)
Bibliographic record
Abstract
The Escherichia coli siderophore enterobactin is synthesized in response to iron starvation. 2,3-Dihydro-2,3-dihydroxybenzoate dehydrogenase (EntA) produces 2,3-dihydroxybenzoate (DHB), a biosynthetic intermediate. 2,3-Dihydroxybenzoate-AMP ligase (EntE) adenylates DHB, activating it for attachment to the NRPS substrate holo-EntB. Using analytical ultracentrifugation, we found that EntA undergoes concentration-dependent dimer-tetramer self-association (K(D) = 12.3 μM). We further found that EntA can form a specific complex with EntE. Pull-down assays revealed that recombinant EntA bait pulled down EntE from E. coli lysates, whereas recombinant EntE bait could pull down EntA. Addition of the SMCC cross-linker to a mixture of EntA and EntE resulted in a cross-linked product with a molecular mass of >250 kDa, suggesting a complex stoichiometry of one EntA tetramer and four EntE monomers. The effect of EntA on EntE activity was also examined. Addition of a 4-fold excess of EntA to an EntE assay mixture resulted in a 6-fold stimulation of EntE activity. EntA was also found to perturb the FRET signal between EntE donor residues and EntE-bound DHB. By following the EntA-dependent decrease in the magnitude of the EntE-DHB FRET signal, EntA-EntE binding behavior was found to be sigmoidal, suggesting the presence of both low- and high-affinity binding sites. The EntA-EntE interaction was also directly measured by isothermal titration calorimetry at 10 °C. The resulting binding isotherm fit well to a model describing two binding sites, supporting our AUC and fluorescence data. Taken together, our data show that tetrameric EntA optimally interacts with EntE, resulting in an enhancement of EntE activity.
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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) | 0.001 | 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 | 0.001 |
| 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".