Specificity of the Synergistic Anion for Iron Binding by Ferric Binding Protein from <i>Neisseria gonorrhoeae</i>
Bibliographic record
Abstract
Ferric binding protein in Neisseria gonorrhoeae (nFbpA) transports iron from outer membrane receptors for host proteins across the periplasm to a permease in an alternative pathway to the use of siderophores in some pathogenic bacteria. Phosphate and nitrilotriacetate, both at pH 8, and vanadate at pH 9 are shown to be synergistic in promoting ferric binding to nFbpA, in contrast to carbonate and sulfate. Interestingly, only phosphate produces the fully closed conformation of nFbpA as defined by native electrophoresis. The role of phosphate was probed by constructing three mutants: Q58E, Q58R, and G140H. The anion and iron binding properties of the Q58E mutant are similar to the wild-type protein, implying that one phosphate oxygen is a hydrogen bond donor and may in part define the specificity of nFbpA for phosphate over sulfate. Phosphate is a weakly synergistic anion in the Q58R and G140H mutants, and these mutants do not form completely closed structures. Ferric binding was investigated by both isothermal titration and differential scanning calorimetry. The apparent affinity of nFbpA for iron in a solution of 30 mM citrate is 1 order of magnitude larger in the presence (K(app)= 1.7 x 10(5) M(-1)) of phosphate than in its absence (K(app) = 1.6 x 10(4) M(-1)) at pH 7. Similar results were obtained at pH 8. This increase in affinity with phosphate as well as the formation of closed structure allows nFbpA to compete for free ferric ions in solution and suggests that ferric binding to nFbpA is regulated by the synergistic phosphate anion at sites of iron uptake.
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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".