Molecular Dynamics Simulation of the Arginine-Assisted Solubilization of Caffeic Acid: Intervention in the Interaction
Bibliographic record
Abstract
We have previously demonstrated that arginine increases the solubility of aromatic compounds that have poor water solubility, an effect referred to as the "arginine-assisted solubilization system (AASS)". In the current study, we utilized a molecular dynamics simulation to examine the solubilization effects of arginine on caffeic acid, which has a tendency to aggregate in aqueous solution. Caffeic acid has a hydrophobic moiety containing a π-conjugated system that includes an aromatic ring and a hydrophilic moiety with hydroxyl groups and a carboxyl group. While its solubility increases at higher pH values due to the acquisition of a negative charge, the solubility was greatly enhanced by the addition of 1 M arginine hydrochloride at any pH. The results of the simulation indicated that the caffeic acid aggregates were dissociated by the arginine hydrochloride, which is consistent with the experimental data. The binding free energy calculation for two caffeic acid molecules in an aqueous 1 M arginine hydrochloride solution indicated that arginine stabilized the dissociated state due to the interaction between its guanidinium group and the π-conjugated system of the caffeic acid. The binding free energy of two caffeic acid molecules in the arginine hydrochloride solution exhibited a local minimum at approximately 8 Å, at which the arginine intervened between the caffeic acid molecules, causing a stabilization of the dissociated state of caffeic acid. Such stabilization by arginine likely led to the caffeic acid solubilization, as observed in both the experiment and the MD simulation. The results reported in this paper suggest that AASS can be attributed to the stabilization resulting from the intervention of arginine in the interaction between the aromatic compounds.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".