Probing aromatic, hydrophobic, and steric effects on the self-assembly of an amyloid-β fragment peptide
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it