The Role of Attractive Non-Covalent Interactions in Peptide Macrocyclization
Bibliographic record
Abstract
The efficiency of macrocyclization reactions relies on the appropriate conformational preorganization of a linear precursor, ensuring that reactive ends are in spatial proximity prior to ring closure. Traditional peptide cyclization approaches that reduce the extent of terminal ion pairing often disfavor cyclization-conducive conformations and can lead to undesired cyclodimerization or oligomerization side reactions, particularly when they are performed without high dilution. To address this challenge, synthetic strategies that leverage attractive noncovalent interactions, such as zwitterionic attraction between chain termini during macrocyclization, offer a potential solution by reducing the entropic penalty associated with linear peptides adopting precyclization conformations. In this study, we investigate the role of ( N -isocyanoimino)triphenylphosphorane (Pinc) in facilitating the cyclization of linear peptides into conformationally rigid macrocycles. The observed moderate diastereoselectivity is consistent with the preferential Si -facial addition of Pinc, where the isocyanide adds to the E -iminium ion on the same face as the l -proline amide group. The resulting peptide chain reveals that the activated phosphonium ylide of Pinc brings the reactive ends close together, promoting cyclization by enclosing the carboxylate within the interior of the pentapeptide and preventing the formation of byproducts. For shorter peptides with modified peptide backbones, the cyclization mechanism and outcome are redirected, as nucleophilic motifs such as thiazole and imidazole can covalently trap nitrilium intermediates. The isolation of the intermediate in the unproductive macrocyclization pathway, along with nuclear magnetic resonance and density functional theory studies, provides insights into heterocycle-dependent selectivity. The Pinc-driven macrocyclization process has generated diverse collections of cyclic molecules, and our models offer a comprehensive understanding of observed trends, facilitating the development of other heterocycle-forming macrocyclization reactions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".