Molecular mechanism of β‐sheet self‐organization at water‐hydrophobic interfaces
Bibliographic record
Abstract
The capacity to form β-sheet structure and to self-organize into amyloid aggregates is a property shared by many proteins. Severe neurodegenerative pathologies such as Alzheimer's disease are thought to involve the interaction of amyloidogenic protein oligomers with neuronal membranes. To understand the experimentally observed catalysis of amyloid formation by lipid membranes and other water-hydrophobic interfaces, we examine the physico-chemical basis of peptide adsorption and aggregation in a model membrane using atomistic molecular simulations. Blocked octapeptides with simple, repetitive sequences, (Gly-Ala)₄, and (Gly-Val)₄, are used as models of β-sheet-forming polypeptide chains found in the core of amyloid fibrils. In the presence of an n-octane phase mimicking the core of lipid membranes, the peptides spontaneously partition at the octane-water interface. The adsorption of nonpolar sidechains displaces the peptides' conformational equilibrium from a heterogeneous ensemble characterized by a high degree of structural disorder toward a more ordered ensemble favoring β-hairpins and elongated β-strands. At the interface, peptides spontaneously aggregate and rapidly evolve β-sheet structure on a 10 to 100 ns time scale, while aqueous aggregates remain amorphous. Catalysis of β-sheet formation results from the combination of the hydrophobic effect and of reduced conformational entropy of the polypeptide chain. While the former drives interfacial partition and displaces the conformational equilibrium of monomeric peptides, the planar interface further facilitates β-sheet organization by increasing peptide concentration and reducing the dimensionality of self-assembly from three to two. These findings suggest a general mechanism for the formation of β-sheets on the surface of globular proteins and for amyloid self-organization at hydrophobic interfaces.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".