Bio-inspired approaches to accelerating metal ion-promoted reactions: enzyme-like rates for metal ion mediated phosphoryl and acyl transfer processes
Bibliographic record
Abstract
Abstract Intense efforts by many research groups for more than 50 years have been directed at biomimetic approaches to understand how enzymes achieve their remarkable rate accelerations. Nevertheless, it was noted in 2003 that, despite numerous efforts to design models for catalyzing the cleavage of such species as phosphate diesters, “none of the several models so far described approaches the enormous catalytic efficiency of natural enzymes”. The same could be said for biomimetics of other enzymes promoting acyl or phosphoryl transfer reactions, particularly those mediated by metal ions such as Zn(II). Clearly other important factors were being overlooked or awaiting discovery. In this manuscript we describe two important effects that we have implemented to accelerate metal ion catayzed phosphoryl and acyl transfer reactions. The first of these relates to a medium effect where the polarity of the solution, as measured by dielectric constant, is reduced from that of water (ε= 78) to values of 31.5 and 24.3 when the solvent is changed to methanol or ethanol. Among organic solvents these light alcohols are closest to water in terms of structure and properties as well as retaining important H-bonding properties. The second important effect involves a known but difficult to demonstrate mode of catalysis where the leaving group (LG) in a solvolysis reaction is accelerated as it becomes progressively poorer. In the cases described herein, the LG’s propensity to depart from a substrate during the course of reaction is accelerated by coordination to a metal ion in a process known as leaving group assistance, or LGA. These two effects can each impart accelerations of 109–1017for certain metal ion catalyzed reactions relative to the corresponding solvent, or base induced reactions.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".