Probing the Effect of Water−Water Interactions on Enzyme Activity with Salt Gradients: A Case-Study Using Ribonuclease t1
Bibliographic record
Abstract
Water molecules interact with one another via hydrogen bonds. Experimental and theoretical evidence indicates that these hydrogen bonds occur in two modalities--high- and low-angle hydrogen bonding--and that the addition of various solutes to water affects only the number of water molecules participating in a specific type of hydrogen bond interactions, not the nature of the water-water interactions. In this work, we have investigated the effect of each of these hydrogen bonding types upon the activity of the enzyme ribonuclease t1. This was done through perturbation of the water hydrogen bonding distribution by using various salts. Our results indicate that various salts differ in their ability to reduce the enzymatic activity of ribonuclease t1, and this ability is well correlated with the ability of each salt to promote high-angle hydrogen bonding in water. By applying the two-phase model of liquid water (i.e., liquid water being modeled as an equilibrium existing between two phases, LD and HD water), we demonstrate that our results are compatible with the assumption that increasing the population of high-angle hydrogen bonds among water molecules stabilizes the more compact, less active conformations of the enzyme. This indicates that the structures that proteins adopt in water solution depend upon the nature of interactions between water molecules.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".