The Response of <i>Escherichia coli</i> NikR to Nickel: A Second Nickel-Binding Site
Bibliographic record
Abstract
The Escherichia coli transcription factor NikR mediates two levels of regulatory control of Ni(II) uptake in response to changes in the levels of available nickel. Despite the evidence that metal binding to two distinct sites on NikR, referred to as the high- and low-affinity Ni(II) sites, is required for Ni(II)-selective DNA binding by the protein, the role of the latter set of Ni(II) ions in the activation of NikR remains controversial, and the position of the putative low-affinity Ni(II)-binding site(s) on NikR has not been determined. In this study we confirm that NikR has a high-affinity Ni(II)-binding site that is maintained upon DNA binding. The ligands of the low-affinity Ni(II)-binding site were examined by using selective chemical modification and mass spectrometry performed in the presence of excess Ni(II) and DNA. We localized this Ni(II) site to a region at the interface between the metal- and DNA-binding domains and identified His48 and His110 as residues that participate in the low-affinity Ni(II)-binding response. Mutation of His48 and His110 to asparagines reduces significantly both NikR's tendency to precipitate in the presence of excess Ni(II) and the affinity of the DNA-bound complex in the presence of excess Ni(II). A complete scheme involving all of the metal-binding sites that contribute to the regulatory function of E. coli NikR in nickel homeostasis is described.
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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 | 0.001 |
| 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".