Converting the Amyloidogenic Islet Amyloid Polypeptide into a Potent Nonaggregating Peptide Ligand by Side Chain-to-Side Chain Macrocyclization
Bibliographic record
Abstract
The islet amyloid polypeptide (IAPP), also known as amylin, is a hormone playing key physiological roles. However, its aggregation and deposition in the pancreatic islets are associated with type 2 diabetes. While this peptide adopts mainly a random coil structure in solution, its secondary conformational conversion into α-helix represents a critical step for receptor activation and contributes to amyloid formation and associated cytotoxicity. Considering the large conformational landscape and high amyloidogenicity of the peptide, as well as the complexity of the self-assembly process, it is challenging to delineate the delicate interplay between helical folding, peptide aggregation, and receptor activation. In the present study, we probed the roles of helical folding on the function-toxicity duality of IAPP by restricting its conformational ensemble through side chain-to-side chain stapling via azide–alkyne cycloaddition. Intramolecular macrocyclization ( i; i + 4) constrained IAPP into α-helix and inhibited its aggregation into amyloid fibrils. These helical derivatives slowed down the self-assembly of unmodified IAPP. Site-specific macrocyclization modulated the capacity of IAPP to perturb lipid bilayers and cell plasma membrane and reduced, or even fully inhibited, the cytotoxicity associated with aggregation. Furthermore, the α-helical IAPP analogs showed moderate to high potency toward cognate G protein-coupled receptors. Overall, these results indicate that macrocyclization represents a promising strategy to protect an amyloidogenic peptide hormone from aggregation and associated toxicity, while maintaining high receptor activity.
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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".