Teraryl Braces in Macrocycles: Synthesis and Conformational Landscape Remodeling of Peptides
Bibliographic record
Abstract
The three-dimensional structure of medium-sized cyclic peptides accounts for their biological activity and other important physiochemical properties. Despite significant advances in the past few decades, chemists' ability to fine-tune the structure, in particular, the backbone conformation, of short peptides made of canonical amino acids is still quite limited. Nature has shown that cross-linking the aromatic side chains of linear peptide precursors via enzyme catalysis can generate cyclophane-braced products with unusual structures and diverse activities. However, the biosynthetic path to these natural products is challenging to replicate in the synthetic laboratory using practical chemical modifications of peptides. Herein, we report a broadly applicable strategy to remodel the structure of homodetic peptides by cross-linking the aromatic side chains of Trp, His, and Tyr residues with various aryl linkers. The aryl linkers can be easily installed via copper-catalyzed double heteroatom-arylation reactions of peptides with aryl diiodides. These aromatic side chains and aryl linkers can be combined to form a large variety of assemblies of heteroatom-linked multi-aryl units. The assemblies can serve as tension-bearable multijoint braces to modulate the backbone conformation of peptides as an entry to previously inaccessible conformational space.
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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 | 0.000 |
| 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".