Understanding the Influence of Guest–Host Interactions on the Conformation of Short Peptides in a Hydrophobic Cavity: A Computational Study
Bibliographic record
Abstract
We performed a computational investigation to understand the conformational preferences of four short peptides in a self-assembled cage based on the experimental work by Y. Hatakeyama et al. (Angew. Chem. Int. Ed.2009, 48, 8695). For this purpose, we combined molecular dynamics simulations, Monte Carlo simulations, and quantum mechanical calculations to obtain energies and structures for several low-lying conformers of four peptides and the corresponding peptide-cage inclusion complexes. Our calculations at both B3LYP and MP2 levels show that for each peptide, the corresponding conformation within the host (as revealed by the crystal structure) does not represent the lowest-energy conformation of this peptide in vacuum. By comparing some low-lying conformers in vacuum and in the cavity (for the same peptide), we found that the cage has a significant influence on the conformational propensities of peptides. First, one carbonyl oxygen of each peptide tends to bind to one Zn(II) atom of the cage, forming a Zn-O bond. The formation of this bond leads to significant charge transfer from the cage to the peptide. Second, this Zn-O bond causes the peptide to go through some local conformational changes. For larger peptides, such as penta- and hexapeptides, our calculations also show that some of their conformers must undergo significant structural changes, due to the confinement of the host. This computational study reveals the noticeable influence of the guest-host interaction on the conformational preferences of short peptides.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".