Spontaneous formation of polyglutamine nanotubes with molecular dynamics simulations
Bibliographic record
Abstract
Expansion of polyglutamine (polyQ) beyond the pathogenic threshold (35-40 Gln) is associated with several neurodegenerative diseases including Huntington's disease, several forms of spinocerebellar ataxias and spinobulbar muscular atrophy. To determine the structure of polyglutamine aggregates we perform replica-exchange molecular dynamics simulations coupled with the optimized potential for effective peptide forcefield. Using a range of temperatures from 250 to 700 K, we study the aggregation kinetics of the polyglutamine monomer and dimer with chain lengths from 30 to 50 residues. All monomers show a similar structural change at the same temperature from alpha-helical structure to random coil, without indication of any significant beta-strand. For dimers, by contrast, starting from random structures, we observe spontaneous formation of antiparallel beta-sheets and triangular and circular beta-helical structures for polyglutamine with 40 residues in a 400 ns 50 temperature replica-exchange molecular dynamics simulation (total integrated time 20 micros). This approximately 32 A diameter structure reorganizes further into a tight antiparallel double-stranded approximately 22 A nanotube with 22 residues per turn close to Perutz' model for amyloid fibers as water-filled nanotubes. This diversity of structures suggests the existence of polymorphism for polyglutamine with possibly different pathways leading to the formation of toxic oligomers and to fibrils.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".