Interplay of Hydrogen Bonds and <i>n</i>→π* Interactions in Proteins
Bibliographic record
Abstract
Protein structures are stabilized by multiple weak interactions, including the hydrophobic effect, hydrogen bonds, electrostatic effects, and van der Waals interactions. Among these interactions, the hydrogen bond is distinct in having its origins in electron delocalization. Recently, another type of electron delocalization, the n→π* interaction between carbonyl groups, has been shown to play a role in stabilizing protein structure. Here we examine the interplay between hydrogen bonding and n→π* interactions. To address this issue, we used data available from high-resolution protein crystal structures to interrogate asparagine side-chain oxygen atoms that are both acceptors of a hydrogen bond and donors of an n→π* interaction. Then we employed natural bond orbital analysis to determine the relative energetic contributions of the hydrogen bonds and n→π* interactions in these systems. We found that an n→π* interaction is worth ~5-25% of a hydrogen bond and that stronger hydrogen bonds tend to attenuate or obscure n→π* interactions. Conversely, weaker hydrogen bonds correlate with stronger n→π* interactions and demixing of the orbitals occupied by the oxygen lone pairs. Thus, these two interactions conspire to stabilize local backbone-side-chain contacts, which argues for the inclusion of n→π* interactions in the inventory of non-covalent forces that contribute to protein stability and thus in force fields for biomolecular modeling.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".