Disaggregation of Islet Amyloid Polypeptide Fibrils as a Potential Anti-Fibrillation Mechanism of Tetrapeptide TNGQ
Bibliographic record
Abstract
Islet amyloid polypeptide (IAPP) fibrillation has been commonly associated with the exacerbation of type 2 diabetes prognosis. Consequently, inhibition of IAPP fibrillation to minimize β-cell cytotoxicity is an important approach towards β-cell preservation and type 2 diabetes management. In this study, we identified three tetrapeptides, TNGQ, MANT, and YMSV, that inhibited IAPP fibrillation. Using thioflavin T (ThT) fluorescence assay, circular dichroism (CD) spectroscopy, dynamic light scattering (DLS), and molecular docking, we evaluated the potential anti-fibrillation mechanism of the tetrapeptides. ThT fluorescence kinetics and microscopy as well as transmission electron microscopy showed that TNGQ was the most effective inhibitor based on the absence of normal IAPP fibrillar morphology. CD spectroscopy showed that TNGQ maintained the α-helical conformation of monomeric IAPP, while DLS confirmed the presence of varying fibrillation species. Molecular docking showed that TNGQ and MANT interact with monomeric IAPP mainly by hydrogen bonding and electrostatic interaction, with TNGQ binding at IAPP surface compared to YMSV, which had the highest docking score, but interact mainly through hydrophobic interaction in IAPP core. The highly polar TNGQ was the most active and appeared to inhibit IAPP fibrillation by disaggregation of preformed IAPP fibrils. These findings indicate the potential of TNGQ in the development of peptide-based anti-fibrillation and antidiabetic nutraceuticals.
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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".