Guiding the Morphology of Amyloid Assemblies by Electrostatic Capping: from Polymorphic Twisted Fibrils to Uniform Nanorods
Bibliographic record
Abstract
Peptides that self-assemble into cross-β-sheet amyloid structures constitute promising building blocks to construct highly ordered proteinaceous materials and nanoparticles. Nevertheless, the intrinsic polymorphism of amyloids and the difficulty of controlling self-assembly currently limit their usage. In this study, the effect of electrostatic interactions on the supramolecular organization of peptide assemblies is investigated to gain insights into the structural basis of the morphological diversities of amyloids. Different charged capping units are introduced at the N-terminus of a potent β-sheet-forming sequence derived from the 20-29 segment of islet amyloid polypeptide, known to self-assemble into polymorphic fibrils. By tuning the charge and the electrostatic strength, different mesoscopic morphologies are obtained, including nanorods, rope-like fibrils, and twisted ribbons. Particularly, the addition of positive capping units leads to the formation of uniform rod-like assemblies, with lengths that can be modulated by the charge number. It is proposed that electrostatic repulsions between N-terminal positive charges hinder β-sheet tape twisting, leading to a unique control over the size of these cytocompatible nanorods by protofilament growth frustration. This study reveals the high susceptibility of amyloid formation to subtle chemical modifications and opens to promising strategies to control the final architecture of proteinaceous assemblies from the peptide sequence.
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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".