Allosteric control of metal-responsive transcriptional regulators in bacteria
Bibliographic record
Abstract
Many transition metals are essential trace nutrients for living organisms, but they are also cytotoxic in high concentrations. Bacteria maintain the delicate balance between metal starvation and toxicity through a complex network of metal homeostasis pathways. These systems are coordinated by the activities of metal-responsive transcription factors-also known as metal-sensor proteins or metalloregulators-that are tuned to sense the bioavailability of specific metals in the cell in order to regulate the expression of genes encoding proteins that contribute to metal homeostasis. Metal binding to a metalloregulator allosterically influences its ability to bind specific DNA sequences through a variety of intricate mechanisms that lie on a continuum between large conformational changes and subtle changes in internal dynamics. This review summarizes recent advances in our understanding of how metal sensor proteins respond to intracellular metal concentrations. In particular, we highlight the allosteric mechanisms used for metal-responsive regulation of several prokaryotic single-component metalloregulators, and we briefly discuss current open questions of how metalloregulators function in bacterial cells. Understanding the regulation and function of metal-responsive transcription factors is a fundamental aspect of metallobiochemistry and is important for gaining insights into bacterial growth and virulence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".