Self-Assembly of the β2-Microglobulin NHVTLSQ Peptide Using a Coarse-Grained Protein Model Reveals a β-Barrel Species
Bibliographic record
Abstract
Although a wide variety of proteins can assemble into amyloid fibrils, the structure of the early oligomeric species on the aggregation pathways remains unknown at an atomic level of detail. In this paper we report, using molecular dynamics simulations with the OPEP coarse-grained force field, the free energy landscape of a tetramer and a heptamer of the beta2-microglobulin NHVTLSQ peptide. On the basis of a total of more than 17 ns trajectories started from various states, we find that both species are in equilibrium between amorphous and well-ordered aggregates with cross-beta-structure, a perpendicular bilayer beta-sheet, and, for the heptamer, six- or seven-stranded closed and open beta-barrels. Moreover, analysis of the heptamer trajectories shows that the perpendicular bilayer beta-sheet is one possible precursor of the beta-barrel, but that this barrel can also be formed from a twisted monolayer beta-sheet with successive addition of chains. Comparison with previous aggregation simulations and the fact that nature constructs transmembrane beta-sheet proteins with pores open the possibility that beta-barrels with small inner diameters may represent a common intermediate during the early steps of aggregation.
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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) | 0.001 | 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".