Probing aromatic, hydrophobic, and steric effects on the self-assembly of an amyloid-β fragment peptide
Bibliographic record
Abstract
Aromatic amino acids have been shown to promote self-assembly of amyloid peptides, although the basis for this amyloid-inducing behavior is not understood. We adopted the amyloid-β 16-22 peptide (Aβ(16-22), Ac-KLVFFAE-NH(2)) as a model to study the role of aromatic amino acids in peptide self-assembly. Aβ(16-22) contains two consecutive Phe residues (19 and 20) in which Phe 19 side chains form interstrand contacts in fibrils while Phe 20 side chains interact with the side chain of Va l18. The kinetic and thermodynamic effect of varying the hydrophobicity and aromaticity at positions 19 and 20 by mutation with Ala, Tyr, cyclohexylalanine (Cha), and pentafluorophenylalanine (F(5)-Phe) (order of hydrophobicity is Ala < Tyr < Phe < F(5)-Phe < Cha) was characterized. Ala and Tyr position 19 variants failed to undergo fibril formation at the peptide concentrations studied, but Cha and F(5)-Phe variants self-assembled at dramatically enhanced rates relative to wild-type. Cha mutation was thermodynamically stabilizing at position 20 (ΔΔG = -0.2 kcal mol(-1) relative to wild-type) and destabilizing at position 19 (ΔΔG = +0.2 kcal mol(-1)). Conversely, F(5)-Phe mutations were strongly stabilizing at both positions (ΔΔG = -1.3 kcal mol(-1) at 19, ΔΔG = -0.9 kcal mol(-1) at 20). The double Cha and F(5)-Phe mutants showed that the thermodynamic effects were additive (ΔΔG = 0 kcal mol(-1) for Cha 19,20 and -2.1 kcal mol(-1) for F(5)-Phe 19,20). These results indicate that sequence hydrophobicity alone does not dictate amyloid potential, but that aromatic, hydrophobic, and steric considerations collectively influence fibril formation.
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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".