Computational Insights into the Formation and Structure of S–N Containing Cyclic Peptides
Bibliographic record
Abstract
Cyclic peptides are known to have biologically important roles and may also be applicable to the pharmaceutical and other industries. Furthermore, thiols and amines, which are found throughout biological systems, can react to form S-N bonds and to date, ∼100 biomolecules containing such a bond have been identified. However, while there are in principle numerous S-N containing peptide-derived rings possible, only a few are presently known to occur in biochemical systems. Density functional theory-based calculations have been used to consider the formation and structure of S-N containing cyclic peptides from systematic series of linear peptides in which a cysteinyl has first been oxidized to a sulfenic or sulfonic acid. In addition, the possible effect of the cysteine's vicinal residue on the free energy of formation has also been considered. In general, when the cysteine is first oxidized to a sulfenic acid, only the formation of smaller S-N containing rings is calculated to be exergonic in aqueous solution. In contrast, when the cysteine is first oxidized to a sulfonic acid, the formation of all rings considered (with one exception) is calculated to be endergonic in aqueous solution. The nature of vicinal residue can influence ring formation through stabilizing or destabilizing intramolecular interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".