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Record W2114793289 · doi:10.1021/jp306661c

All-Atom Stability and Oligomerization Simulations of Polyglutamine Nanotubes with and without the 17-Amino-Acid N-Terminal Fragment of the Huntingtin Protein

2012· article· en· W2114793289 on OpenAlexaff
Sébastien Côté, Guanghong Wei, Normand Mousseau

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2012
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHuntingtinMolecular dynamicsHuntingtin ProteinAmino acidAntiparallel (mathematics)ChemistryProtein structureGlutamineProtein aggregationBiophysicsCrystallographyBiochemistryBiologyComputational chemistryGenePhysics

Abstract

fetched live from OpenAlex

Several neurodegenerative diseases are associated with the polyglutamine (polyQ) repeat disorder in which a segment of consecutive glutamines in the native protein is produced with too many glutamines. Huntington's disease, for example, is related to the misfolding of the Huntingtin protein which occurs when the polyQ segment has more than approximately 36 glutamines. Experimentally, it is known that the polyQ segment alone aggregates into β-rich conformations such as amyloid fibrils. Its aggregation is modulated by the number of glutamine residues as well as by the surrounding amino acid sequences such as the 17-amino-acid N-terminal fragment of Huntingtin which increases the aggregation rate. Little structural information is available, however, regarding the first steps of aggregation and the atomistic mechanisms of oligomerization are yet to be described. Following previous coarse-grained replica-exchange molecular dynamics simulations that show the spontaneous formation of a nanotube consisting of two intertwined antiparallel strands (Laghaei, R.; Mousseau, N. J. Chem. Phys. 2010, 132, 165102), we study this configuration and some extensions of it using all-atom explicit solvent MD simulations. We compare two different lengths for the polyQ segment, 40 and 30 glutamines, and we investigate the impact of the Huntingtin N-terminal residues (htt(NT)). Our results show that the dimeric nanotubes can provide a building block for the formation of longer nanotubes (hexamers and octamers). These longer nanotubes are characterized by large β-sheet propensities and a small solvent exposure of the main-chain atoms. Moreover, the oligomerization between two nanotubes occurs through the formation of protein/protein H-bonds and can result in an elongation of the water-filled core. Our results also show that the htt(NT) enhances the structural stability of the β-rich seeds, suggesting a new mechanism by which it can increase the aggregation rate of the amyloidogenic polyQ sequence.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.020
GPT teacher head0.261
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2012
Admission routes1
Has abstractyes

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