Contribution of the 12–17 hydrophobic region of islet amyloid polypeptide in self-assembly and cytotoxicity
Bibliographic record
Abstract
The islet amyloid polypeptide (IAPP) is a 37-residue aggregation-prone peptide hormone whose deposition as insoluble fibrils in the islets of Langerhans is associated with type II diabetes. Therapeutic interventions targeting IAPP amyloidogenesis, which contributes to pancreatic β-cell degeneration, remain elusive owing to the lack of understanding of the self-assembly mechanisms and of the quaternary proteospecies mediating toxicity. While countless studies have investigated the contributions of the 20–29 amyloidogenic core in self-assembly, IAPP central region, i.e. positions 11 to 19, has been less studied, notwithstanding its potential key role in oligomerization. In this context, the present study aimed at investigating the physicochemical and conformational properties driving IAPP self-assembly and associated cytotoxicity. Computational tools and all-atom molecular dynamics simulation suggested that the hydrophobic 12–17 segment promotes IAPP self-recognition and aggregation. Alanine scanning revealed that the hydrophobic side chains of Leu12, Phe15 and Val17 are critical for amyloid fibril formation. Destabilization of the α-helical folding by Pro substitution enhanced self-assembly when the pyrrolidine ring was successively introduced at positions Ala13, Asn14 and Phe15, in comparison to respective Ala-substituted counterparts. Modulating the peptide backbone flexibility at position Leu16 through successive incorporation of Pro, Gly and α-methylalanine, inhibited amyloid formation and reduced cytotoxicity, while the isobutyl side chain of Leu16 was not critical for self-assembly and IAPP-mediated toxicity. These results highlight the importance of the 12–17 hydrophobic region of IAPP for self-recognition, ultimately supporting the development of therapeutic approaches to prevent oligomerization and/or fibrillization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".