Characterization of Aromatic−Amide(Side-Chain) Interactions in Proteins through Systematic ab Initio Calculations and Data Mining Analyses
Bibliographic record
Abstract
In this study, noncovalent interactions between aromatic groups and side-chain amides in proteins were characterized. To elucidate the nature and structure−strength relationship of the interaction, the geometries and interaction potential energy surfaces for the benzene−formamide model complex were exhaustively and systematically studied at the MP2 level of theory. The effects of basis set size and basis set superposition error were investigated for 15 selected complex structures. The results indicate that the aromatic−amide (side-chain) interaction can achieve a significant binding energy of up to 4.0 kcal/mol over a wide conformational space. The interaction involves the entire side-chain amide group rather than only its amine portion. Both dispersion and electrostatic interactions are the major contributors for the binding energy, and the π electron charge distributions in both groups and the dipole moment of the side-chain amide group are crucial to the interaction. The importance of such an interaction in proteins was verified through data mining analyses of 1029 X-ray protein structures. The interaction naturally occurs in proteins with a frequency of more than one per two proteins on a statistical average and is of significance for some protein structure. The interaction was also found to play a role in determining the biological activity of some proteins. Our study not only emphasizes the significance of aromatic−amide(side-chain) interactions in proteins but also deepens our understanding of noncovalent interactions involving benzene or other aromatic groups.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".