Kinetic and Conformational Insights into Islet Amyloid Polypeptide Self-Assembly Using a Biarsenical Fluorogenic Probe
Bibliographic record
Abstract
Amyloid fibril formation and tissue deposition are associated with many diseases. Studies have shown that prefibrillar intermediates, such as oligomers, are the most toxic proteospecies of the amyloidogenic cascade. Thus, understanding the mechanisms of formation and the conformational ensemble of prefibrillar species is critical. Due to their transient and heterogeneous nature, detection and characterization of prefibrillar species remain challenging. The fluorogenic probe fluorescein arsenical hairpin (FlAsH), which recognizes a tetracysteine motif, has been recently used to detect the oligomerization of amyloidogenic peptides encompassing a Cys-Cys tag. In this study, we extended the FlAsH detection method to gain novel kinetic and conformational insights into the self-assembly of islet amyloid polypeptide (IAPP), a 37-residue peptide hormone whose deposition is associated with type II diabetes. By positional scanning of the Cys-Cys motif, the stability of the noncontiguous tetracysteine FlAsH-binding sites formed during self-assembly was evaluated and revealed rapid monomer self-recognition through the convergence of C-terminal domains. On the other hand, the N-terminal domains come close to each other only upon the formation of the cross-β-sheet amyloid structure. We demonstrated that this method is well-suited to detect thioflavin T-negative fibrils and to screen inhibitors of amyloid formation. This study highlights that with positional scanning of the split-tetracysteine motif (Cys-Cys), the FlAsH detection method offers unique time-dependent conformational insights on the proteospecies assembled throughout the amyloidogenic pathway.
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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".