Bibliographic record
Abstract
Although the vast majority of peptide bonds in folded proteins are found in the trans conformation, a small percentage are found in the less energetically favorable cis conformation. Though the mechanism of cis peptide bond formation remains unknown, the role of local aromatics has been emphasized in the literature. This paper presents results from a comprehensive statistical analysis of both the local and nonlocal (i.e., tertiary) environment around cis peptides. In addition to an increased frequency of aromatic residues in the local environment around cis peptides, a number of nonlocal differences in protein secondary and tertiary structure between cis and trans peptides are found: (i) coil regions containing cis peptides are almost twice as long as those without cis peptides and include more Tyr and Pro residues; (ii) cis peptides occur with high frequencies in coil regions near large beta-structures; (iii) there is a nonlocal enrichment of Cys, His, Tyr, and Ser in the tertiary environment surrounding cis peptides when compared to trans peptides; and (iv) on average, cis peptides make fewer medium-range and more long-range contacts than trans peptides do. On the basis of these observations, it is concluded that nonlocal factors play a significant role in cis peptide formation, which has not been fully appreciated previously. An autocatalytic model for cis peptide formation is discussed as are consequences for protein folding.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".