Zinc Stoichiometry of Yeast RNA Polymerase II and Characterization of Mutations in the Zinc-binding Domain of the Largest Subunit
Bibliographic record
Abstract
Atomic absorption spectroscopy demonstrated that highly purified RNA polymerase II from the yeast Saccharomyces cerevisiae binds seven zinc ions. This number agrees with the number of potential zinc-binding sites among the 12 different subunits of the enzyme and with our observation that the ninth largest subunit alone is able to bind two zinc ions. The zinc-binding motif in the largest subunit of the enzyme was investigated using mutagenic analysis. Altering any one of the six conserved residues in the zinc-binding motif conferred either a lethal or conditional phenotype, and zinc blot analysis indicated that mutant forms of the domain had a 2-fold reduction in zinc affinity. Mutations in the zinc-binding domain reduced RNA polymerase II activity in cell-free extracts, even though protein blot analysis indicated that the mutant subunit was present in excess of wild-type levels. Purification of one mutant RNA polymerase revealed a subunit profile that was wild-type like with the exception of two subunits not required for core enzyme activity (Rpb4p and Rpb7p), which were missing. Core activity of the mutant enzyme was reduced 20-fold. We conclude that mutations in the zinc-binding domain can reduce core activity without altering the association of any of the subunits required for this activity.
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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) | 0.000 | 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".