Probing the relationships between self-assembly and the antimicrobial activity of amyloidogenic peptides: The islet amyloid polypeptide as a case study
Bibliographic record
Abstract
Antimicrobial peptides (AMPs) are key components of the innate immune system across diverse organisms. Interestingly, some AMPs can adopt β-sheet secondary structure and self-assemble into amyloid-like fibrils. Recent works have also revealed that amyloidogenic peptides exhibit antimicrobial properties and share a common mechanism of plasma membrane perturbation with AMPs. In this study, we explored the relationships between the antimicrobial activity of amyloidogenic peptides and their self-assembly by using the islet amyloid polypeptide (IAPP) as a model. IAPP is an aggregation-prone 37-residue hormone whose pancreatic deposition and accumulation are associated with type II diabetes. Antimicrobial assays revealed that IAPP monomers and prefibrillar aggregates, including soluble oligomers, inhibit the growth of Escherichia coli and Staphylococcus epidermidis. Additionally, monomeric and prefibrillar proteospecies perturbed anionic lipid vesicles that mimic bacterial plasma membrane and decrease the metabolic activity. In contrast, pre-assembled amyloid fibrils exhibited weak antimicrobial activities and lipid membrane perturbation, although they agglutinated bacteria avidly. By taking advantage of residue-specific substitutions that modulate the aggregation propensity, we observed that derivatives with hindered amyloidogenicity retained antimicrobial activities, while those with accelerated kinetics of amyloid self-assembly had weaker antimicrobial effect. Moreover, by modulating the propensity of IAPP to fold into an α-helix, we observed that amyloid formation is not a prerequisite for the antimicrobial activity, while the destabilization of helical folding reduced IAPP antimicrobial activity. This study provides fundamental mechanistic insights of the modest antimicrobial activity of IAPP and highlights that precaution should be taken before generalizing the antimicrobial potential of self-assembling amyloid polypeptides.
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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 | 0.001 |
| 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".