Stretching Peptides
Bibliographic record
Abstract
Advances in the modulation of protein-protein interactions (PPIs) enable the characterization of PPI networks that govern disease mechanisms and guide the design of novel therapeutics and probes. These PPIs are often characterized by complementary binding to shallow protein surfaces that are challenging to target using standard methods for high-affinity small molecule ligand generation. Compared to linear peptides, synthetically constrained epitopes provide an energetic advantage for binding PPI surfaces by decreasing unbound-state entropy. Such peptide stapling strategies promoting α-helix and β-hairpin structures are well developed. However, approaches for accessing common extended backbone structures are limited. Here we demonstrate the incorporation of a rigid, linear, diyne brace between side chains at the i to i+2 positions to generate a family of low molecular weight peptide macrocycles adopting extended backbones. We show by NMR and DFT studies that these ‘stretched peptides’ adopt rigid and stable conformations in solution which can be tuned to explore a wide range of extended peptide conformational space, including those inherent to β-strands and polyproline II (PPII) helices. The formation of the diyne brace is accomplished in excellent conversions (>95%) and is amenable to high throughput synthesis. The minimalist structure-inducing tripeptide core (<300 Da) is amenable to further synthetic elaboration to optimize bioactivity and pharmacokinetics. We showcase the utility of diyne-braced peptides with the synthesis of macrocyclic inhibitors of bacterial signal 1 peptidase.
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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) | 0.012 | 0.004 |
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".