Understanding Ion‐specific “Hofmeister” Effects in Enzyme Catalysis through using RNase A as a Paradigm Model
Bibliographic record
Abstract
Biophysical studies in the last two decades have clearly demonstrated that salts affect biomolecules in an ion-specific manner (i. e., Hofmeister Effects). Studies performed upon such diverse biological processes such as protein folding, protein precipitation, protein coacervation and phase separation, and protein oligomerization, have all shown that this ion specificity is directly related to how individual ions interact with biomolecular surfaces. Interestingly, although ion-specific effects upon enzyme catalytic processes are well-known in the literature, a molecular level description of these effects has not yet been made available. For example, it is not clear whether ion-specific effects observed in enzyme catalysis are directly related to how ions modulate the enzyme's folding free energy, or not. This work attempts to address this need by investigating ion-specific effects upon the enzymatic activity and folding free energy of a well-characterized enzyme system, Ribonuclease A (RNase A). To this end we have developed a robust framework to analyze and quantify ion-specific effects upon the RNase A catalyzed phosphate ring opening reaction of cCMP (Cytidine 2':3'-cyclic monophosphate monosodium salt). Our studies show that both the folding thermodynamics and the Michaelis-Menten kinetic parameters of this enzyme show ion-specific salt dependence. However, even through salt addition affects the folding free energy and enzyme catalysis of RNase A in an ion-specific manner, these effects are not necessarily directly related to each other. Ion-specific effects observed in protein folding reflects mostly how an individual ion interacts with the overall protein surface; while alternatively, ion-specific effects on enzyme activity indicate how a given ion interacts with the enzyme active site surface or alternatively, how ions interact with the substrate molecule as represented by changes in the substrate thermodynamic activity coefficient.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".