Self-assembly of amyloid-forming peptides by molecular dynamics simulations
Bibliographic record
Abstract
Protein aggregation is associated with many neurodegenerative diseases. Understanding the aggregation mechanisms is a fundamental step in order to design rational drugs interfering with the toxic intermediates. This self-assembly process is however difficult to observe experimentally, which gives simulations an important role in resolving this problem. This study shows how we can proceed to gain knowledge about the first steps of aggregation. We first start by characterizing the free energy surface of the Abeta (16-22) dimer, a well-studied system numerically, using molecular dynamics simulations with OPEP coarse-grained force field. We then turn to the study of the NHVTLSQ peptide in 4-mers and 16-mers, extracting information on the onset of aggregation. In particular, the simulations indicate that the peptides are mostly random coil at room temperature, but can visit diverse amyloid-competent topologies, albeit with a low probability. The fact that the 16-mers constantly move from one structure to another is consistent with the long lag phase measured experimentally, but the rare critical steps leading to the rapid formation of amyloid fibrils still remain to be determined.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".