Identifying Structural Features of Fibrillar Islet Amyloid Polypeptide Using Site-directed Spin Labeling
Bibliographic record
Abstract
Pancreatic amyloid deposits, composed primarily of the 37-residue islet amyloid polypeptide (IAPP), are a characteristic feature found in more than 90% of patients with type II diabetes. Although IAPP amyloid deposits are associated with areas of pancreatic islet beta-cell dysfunction and depletion and are thought to play a role in disease, their structure is unknown. We used electron paramagnetic resonance spectroscopy to analyze eight spin-labeled derivatives of IAPP in an effort to determine structural features of the peptide. In solution, all eight derivatives gave rise to electron paramagnetic resonance spectra with sharp lines indicative of rapid motion on the sub-nanosecond time scale. These spectra are consistent with a rapidly tumbling and highly dynamic peptide. In contrast, spectra for the fibrillar form exhibit reduced mobility and the presence of strong intermolecular spin-spin interactions. The latter implies that the peptide subunits are ordered and that the same residues from neighboring peptides are in close proximity to one another. Our data are consistent with a parallel arrangement of IAPP peptides within the amyloid fibril. Analysis of spin label mobility indicates a high degree of order throughout the peptide, although the N-terminal region is slightly less ordered. Possible similarities with respect to the domain organization and parallelism of Alzheimer's amyloid beta peptide fibrils are discussed.
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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) | 0.000 | 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 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".