Selectivity of Metal Binding and Metal-Induced Stability of <i>Escherichia coli</i> NikR
Bibliographic record
Abstract
NikR from Escherichia coli is a nickel-responsive transcription factor that regulates the expression of a nickel ion transporter. Metal analysis reveals that NikR can bind a variety of divalent transition metals, including Ni(II), Cu(II), Zn(II), Co(II), and Cd(II). The selectivity of metal binding to NikR was investigated by using electronic absorption spectroscopy and small-molecule competitors. The relative affinities, Mn(II) < Co(II) < Ni(II) < Cu(II) > or = Zn(II), follow the Irving-Williams series of metal-complex stabilities. Similar metal affinities were measured for the isolated metal-binding domain of NikR. To determine if any of these metal ions confer a differential effect on NikR, the stability of the metal-bound complexes was examined. In both thermal and chemical denaturation experiments, nickel binding stabilizes the protein more than any of the other metals tested. Thermal denaturation experiments indicate that metal dissociation occurs after loss of secondary structure, but there was no evidence for metal binding to unfolded protein following reversible chemical denaturation. These experiments demonstrate that, although several different metals can bind to NikR, nickel exerts a selective allosteric effect. The implications of these experiments on the in vivo role of NikR as a nickel metalloregulator are discussed.
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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 | 0.001 |
| 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".