Evaluating How Discrete Water Molecules Affect Protein–DNA π–π and π<sup>+</sup>–π Stacking and T-Shaped Interactions: The Case of Histidine-Adenine Dimers
Bibliographic record
Abstract
Changes in the magnitude of (M06-2X/6-31+G(d,p)) π-π stacking and T-shaped (nucleobase-edge and amino acid-edge) interactions between (neutral or protonated) histidine (His) and adenine (A) dimers upon microsolvation with up to four discrete water molecules were determined. A variety of histidine-water interactions were considered including conventional (N-H···O, N···H-O, C-H···O) hydrogen bonding and nonconventional (X-H···π (neutral His) or lone-pair···π (protonated His)) contacts. Overall, the effects of discrete His-H(2)O interactions on the neutral histidine-adenine π-π contacts are negligible (<3 kJ mol(-1) or 15%) regardless of the type of water binding, the number of water molecules bound, or the His-A dimer (stacked or (amino acid- or nucleobase-edge) T-shaped) configuration. This suggests that previously reported gas-phase binding strengths for a variety of neutral amino acid-nucleobase dimers are likely relevant for a wide variety of (microsolvated) environments. In contrast, the presence of water decreases the histidine-adenine π(+)-π interaction by up to 15 kJ mol(-1) (or 30%) for all water binding modes and orientations, as well as different stacked and T-shaped His(+)-A dimers. Regardless of the larger effect of discrete histidine-water interactions on the magnitude of the π(+)-π compared with π-π interactions, the π(+)-π binding strengths remain substantially larger than the corresponding π-π contacts. These findings emphasize the distinct nature of π(+)-π and π-π interactions and suggest that π(+)-π contacts can provide significant stabilization in biological systems relative to π-π contacts under many different environmental conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 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".