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Record W2765531494 · doi:10.1021/acs.jcim.7b00452

Inhibition of β-Amyloid Channels with a Drug Candidate wgx-50 Revealed by Molecular Dynamics Simulations

2017· article· en· W2765531494 on OpenAlexaff
Shuang Hou, Ruo‐Xu Gu, Dong‐Qing Wei

Bibliographic record

VenueJournal of Chemical Information and Modeling · 2017
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsUniversity of Calgary
FundersMinistry of Science and Technology of the People's Republic of China
KeywordsMolecular dynamicsBiophysicsIon channelChemistryDrug discoveryMechanism (biology)ExtracellularConductanceAmyloid (mycology)BiochemistryBiologyComputational chemistryReceptorPhysics

Abstract

fetched live from OpenAlex

Destabilization of cellular ionic homeostasis by toxic β-amyloid (Aβ) channels/barrels, which is a pathogenic mechanism for Alzheimer's disease (AD), is inhibited by a novel anti-AD drug candidate wgx-50 significantly in our previous biological experiments. In this work, molecular dynamics simulations are conducted to investigate wgx-50-Aβ channels/barrels interactions, as well as the ion conductance inhibition mechanism. Ion influx from the extracellular side to the central pore, which is found in apo-form simulations, is blocked by wgx-50 ligands that bind to the hydrophobic rings at the entrance of the channels/barrels. The wgx-50 binding results in smaller pore diameter of the channels/barrels; however, the overall morphology of them remains unaffected in accessible simulation time. The wgx-50 binding site in this work is consistent with what we found in our previous simulations of Aβ protofibril. Our work not only investigates the ligand-Aβ channels/barrels interaction mechanism but also provides insights into the rational drug design of Alzheimer's disease.

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)0.000
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 score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.301
Teacher spread0.284 · 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

Citations52
Published2017
Admission routes1
Has abstractyes

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