Peptide Mimics of the Cysteine‐Rich Regions of HapX and SreA Bind a [2Fe‐2S] Cluster In Vitro
Bibliographic record
Abstract
HapX and SreA are transcription factors that regulate the response of the fungus Aspergillus fumigatus to the availability of iron. During iron starvation, HapX represses genes involved in iron consuming pathways and upon a shift to iron excess, HapX activates these same genes. SreA blocks the expression of genes needed for iron uptake during periods of iron availability. Both proteins possess cysteine-rich regions (CRR) that are hypothesized to be necessary for the sensing of iron levels. However, the contribution of each of these domains to the function of the protein has remained unclear. Here, the ability of peptide analogs of each CRR is determined to bind an iron-sulfur cluster in vitro. UV-vis and resonance Raman (RR) spectroscopies reveal that each CRR is capable of coordinating a [2Fe-2S] cluster with comparable affinities. The iron-sulfur cluster coordinated to the CRR-B domain of HapX displays particularly high stability. The data are consistent with HapX and SreA mediating responses to cellular iron levels through the direct coordination of [2Fe-2S] clusters. The high stability of the CRR-B peptide may also find use as a starting point for the development of new green catalysts.
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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) | 0.001 | 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) | 0.002 | 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".