Cation−π Interactions between Quaternary Ammonium Ions and Amino Acid Aromatic Groups in Aqueous Solution
Bibliographic record
Abstract
Cation−π interactions play important roles in the stabilization of protein structures and protein–ligand complexes. They contribute to the binding of quaternary ammonium ligands (mainly RNH 3 + and RN(CH 3 ) 3 + ) to various protein receptors and are likely involved in the blockage of potassium channels by tetramethylammonium (TMA + ) and tetraethylammonium (TEA + ). Polarizable molecular models are calibrated for NH 4 +, TMA +, and TEA + interacting with benzene, toluene, 4-methylphenol, and 3-methylindole (representing aromatic amino acid side chains) based on the ab initio MP2(full)/6-311++G(d,p) properties of the complexes. Whereas the gas-phase affinity of the ions with a given aromatic follows the trend NH 4 + > TMA + > TEA +, molecular dynamics simulations using the polarizable models show a reverse trend in water, likely due to a contribution from the hydrophobic effect. This reversed trend follows the solubility of aromatic hydrocarbons in quaternary ammonium salt solutions, which suggests a role for cation−π interactions in the salting-in of aromatic compounds in solution. Simulations in water show that the complexes possess binding free energies ranging from −1.3 to −3.3 kcal/mol (compared to gas-phase binding energies between −8.5 and −25.0 kcal/mol). Interestingly, whereas the most stable complexes involve TEA + (the largest ion), the most stable solvent-separated complexes involve TMA + (the intermediate-size ion).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.003 | 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".