Exploring the Role of Solvent on Carbohydrate–Aryl Interactions by Diffusion NMR-Based Studies
Bibliographic record
Abstract
Carbohydrate-protein interactions play an important role in many molecular recognition processes. An exquisite combination of multiple factors favors the interaction of the receptor with one specific type of sugar, whereas others are excluded. Stacking CH-aromatic interactions within the binding site provide a relevant contribution to the stabilization of the resulting sugar-protein complex. Being experimentally difficult to detect and analyze, the key CH-π interaction features have been very often dissected using a variety of techniques and simple model systems. In the present work, diffusion NMR spectroscopy has been employed to separate the components of sugar mixtures in different solvents on the basis of their differential ability to interact through CH-π interactions with one particular aromatic cosolute in solution. The experimental data show that the properties of the solvent did also influence the diffusion behavior of the sugars present in the mixture, inhibiting or improving their separation. Overall, the results showed that, for the considered monosaccharide derivatives, their diffusion coefficient values and, consequently, their apparent molecular sizes and/or shapes depend on the balance between solute/cosolute as well as solute/solvent interactions. Thus, in certain media and in the presence of the aromatic cosolute, the studied saccharides that are more suited to display CH-π interactions exhibited a lower diffusion coefficient than the noncomplexing sugars in the mixture. However, when dissolved in another medium, the interaction with the solvent strongly competes with that of the aromatic cosolute.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".