Solvent Interactions Stabilize the Polyproline II Conformation of Glycosylated Oligoprolines
Bibliographic record
Abstract
In nature, proline residues carry several post-translational modifications (PTMs), including 4R hydroxylation and glycosylation. A recent study synthesized contiguously hydroxylated and glycosylated nonaproline peptides and revealed that both PTMs lead to a significant increase in the thermal stability of PPII relative to the unmodified oligoproline. The increased stability of the hydroxylated peptide can be explained by increased stability of the trans isomer due to stereoelectronic effects. However, the effects of glycosylation cannot be completely explained by stereoelectronics since previous experimental results indicate that 4R-glycosylation does not produce observable changes in the trans preference compared to 4R-hydroxylation. We therefore used sophisticated molecular modeling techniques to determine the reason for the further increase in thermal stability upon glycosylation. Free energy estimates obtained from adaptively biased molecular dynamics calculations in implicit (explicit) solvent are -9 kcal mol(-1) (-20 kcal mol(-1)) for the hydroxylated compound and -9 kcal mol(-1) (-46 kcal mol(-1)) for the glycosylated compound, indicating that direct solvent-peptide interactions are vital for explaining the glycosylation effects on PPII stability. Our data reveals for the first time that interactions between the hydroxyl groups in the glycosylated compound and water act in a complementary fashion with stereoelectronic effects to stabilize the PPII conformation in these substituted oligoproline peptides.
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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 | 0.000 |
| 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 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".