<sup>1</sup>H NMR studies on the solution conformation of the [L-Ser<sup>10</sup>] and [D-Ser<sup>10</sup>] analogues of contulakin-G
Bibliographic record
Abstract
The synthesis of the O-glycosylated serine-10 analogue of contulakin-G yielded both the [L-] and the [D-Ser10] analogues. The 1H NMR study indicated that the sugars of the two Ser10-glycosylated peptides lacked the hydrogen bond to the peptide backbone that exists in contulakin-G. NOEs showed that the glycan part of the [D-Ser10] analogue had a different orientation to the peptide backbone than that of the [L-Ser10] analogue. The peptide backbones in the two compounds were found to exist mainly in random coil conformations, with transient turns at the site of glycosylation. A transient turn was also found at the C-terminus of the [D-Ser10] glycopeptide. The NMR data indicated that the average conformation of the [D-Ser10] analogue resembles the conformation of contulakin-G more than the [L-Ser] does. Since biological data showed that the [D-Ser10] glycopeptide was as active as contulakin-G, while the [L-Ser10] glycopeptide was only slightly active at more than 100 times the dose, it is possible that it is the orientation of the glycan relative to the peptide chain that is actually recognized by the proteolytic enzyme.Key words: conformation, contulakin-G analogues, NMR, O-linked glycopeptide.
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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 | 0.000 |
| 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) | 0.001 | 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".