A Fungal Family of Transcriptional Regulators: the Zinc Cluster Proteins
Bibliographic record
Abstract
The trace element zinc is required for proper functioning of a large number of proteins, including various enzymes. However, most zinc-containing proteins are transcription factors capable of binding DNA and are named zinc finger proteins. They form one of the largest families of transcriptional regulators and are categorized into various classes according to zinc-binding motifs. This review focuses on one class of zinc finger proteins called zinc cluster (or binuclear) proteins. Members of this family are exclusively fungal and possess the well-conserved motif CysX(2)CysX(6)CysX(5-12)CysX(2)CysX(6-8)Cys. The cysteine residues bind to two zinc atoms, which coordinate folding of the domain involved in DNA recognition. The first- and best-studied zinc cluster protein is Gal4p, a transcriptional activator of genes involved in the catabolism of galactose in the budding yeast Saccharomyces cerevisiae. Since the discovery of Gal4p, many other zinc cluster proteins have been characterized; they function in a wide range of processes, including primary and secondary metabolism and meiosis. Other roles include regulation of genes involved in the stress response as well as pleiotropic drug resistance, as demonstrated in budding yeast and in human fungal pathogens. With the number of characterized zinc cluster proteins growing rapidly, it is becoming more and more apparent that they are important regulators of fungal physiology.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".