The “metallo-specific” response of proteins: A perspective based on the Escherichia coli transcriptional regulator NikR
Bibliographic record
Abstract
Transition metal ions are required by all cells but an excess of metal ions beyond physiological requirements has toxic consequences. Optimal cellular concentrations of transition metals are commonly maintained by metal-responsive transcription factors that regulate genes encoding the proteins responsible for transport, sequestration and/or use of the metals. These metalloregulators must discriminate between the bioavailable metals to properly effect metal homeostasis, but how this metal selectivity is achieved is poorly understood. This perspective examines the metal-selective response of the Escherichia coli Ni(II)-responsive metalloregulator NikR. Biochemical and structural studies of E. coli NikR reveal that the mechanism of metal-selective regulation is more complex than that defined by simple metal-binding thermodynamics. Here we examine the metal-dependent allosteric changes on NikR structure that affect DNA binding and discuss the correspondence with other metalloregulators. Given what we have learned of how metal selectivity is achieved by E. coli NikR, we propose a complete scheme for the regulatory function of NikR in E. coli nickel homeostasis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".