Altering the Divalent Metal Ion Preference of RNase E
Bibliographic record
Abstract
RNase E is a major intracellular endoribonuclease in many bacteria and participates in most aspects of RNA processing and degradation. RNase E requires a divalent metal ion for its activity. We show that only Mg(2+) and Mn(2+) will support significant rates of activity in vitro against natural RNAs, with Mn(2+) being preferred. Both Mg(2+) and Mn(2+) also support cleavage of an oligonucleotide substrate with similar kinetic parameters for both ions. Salts of Ni(2+) and Zn(2+) permitted low levels of activity, while Ca(2+), Co(3+), Cu(2+), and Fe(2+) did not. A mutation to one of the residues known to chelate Mg(2+), D346C, led to almost complete loss of activity dependent on Mg(2+); however, the activity of the mutant enzyme was fully restored by the presence of Mn(2+) with kinetic parameters fully equivalent to those of wild-type enzyme. A similar mutation to the other chelating residue, D303C, resulted in nearly full loss of activity regardless of metal ion. The properties of RNase E D346C enabled a test of the ionic requirements of RNase E in vivo. Plasmid shuffling experiments showed that both rneD303C (i.e., the rne gene encoding a D-to-C change at position 303) and rneD346C were inviable whether or not the selection medium was supplied with MnSO4, implying that RNase E relies on Mg(2+) exclusively in vivo.
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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 | 0.001 |
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".