A Reductionist Biomimetic Model System That Demonstrates Highly Effective Zn(II)-Catalyzed Cleavage of an RNA Model
Bibliographic record
Abstract
The cyclization of the RNA model 2-hydroxypropyl p-nitrophenyl phosphate (HPNPP, 1) promoted by Zn2+ alone and the 1,5,9-triazacyclododecane complex of Zn2+ (Zn2+:[12]aneN3) is studied in ethanol in the presence of 0.5 equiv of -OEt/Zn2+ to investigate the effect of a low polarity/dielectric medium on a metal-catalyzed reaction of biological relevance. Ethanol exerts a medium effect that promotes strong binding of HPNPP to Zn2+, followed by a dimerization to form a catalytically active complex (HPNPP:Zn2+)2 in which the phosphate undergoes cyclization with a rate constant of kcat = 2.9 s(-1) at s(s)pH 7.1. In the presence of the triaza ligand:Zn2+ complex, the change from water to methanol and then to ethanol brings about a mechanism where two molecules of the complex, suggested as EtOH:Zn2+:[12]aneN3 and its basic form, EtO-:Zn2+:[12]aneN3, bind to HPNPP and catalyze its decomposition with a rate constant of kcat of 0.13 s(-1) at s(s)pH 7.1. Overall, the acceleration exhibited in these two situations is 4 x 10(14)-fold and 1.7 x 10(12)-fold relative to the background ethoxide-promoted reactions at the respective s(s)pH values. The implications of these findings are discussed within the context of the idea that enzymatic catalysis is enhanced by a reduced effective dielectric constant within the active site.
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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".