Thermodynamics and dynamics of amyloid peptide oligomerization are sequence dependent
Bibliographic record
Abstract
Aggregation of the full-length amyloid-beta (Abeta) and beta2-microglobulin (beta2m) proteins is associated with Alzheimer's disease and dialysis-related amyloidosis, respectively. This assembly process is not restricted to full-length proteins, however, many short peptides also assemble into amyloid fibrils in vitro. Remarkably, the kinetics of amyloid-fibril formation of all these molecules is generally described by a nucleation-polymerization process characterized by a lag phase associated with the formation of a nucleus, after which fibril elongation occurs rapidly. In this study, we report using long molecular dynamics simulations with the OPEP coarse-grained force field, the thermodynamics and dynamics of the octamerization for two amyloid 7-residue peptides: the beta2m83-89 NHVTLSQ and Abeta16-22 KLVFFAE fragments. Based on multiple trajectories run at 310 K, totaling 2.2 mus (beta2m83-89) and 4.8 mus (Abeta16-22) and starting from random configurations and orientations of the chains, we find that the two peptides not only share common but also very different aggregation properties. Notably, an increase in the hydrophobic character of the peptide, as observed in Abeta16-22 with respect to beta2m83-89 impacts the thermodynamics by reducing the population of bilayer beta-sheet assemblies. Higher hydrophobicity is also found to slow down the dynamics of beta-sheet formation by enhancing the averaged lifetime of all configuration types (CT) and by reducing the complexity of the CT transition probability matrix. Proteins 2009. (c) 2008 Wiley-Liss, Inc.
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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 | 0.001 |
| 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 | 0.001 |
| 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".