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Record W2046293703 · doi:10.1016/j.jalz.2011.05.1383

P2‐512: Identifying endogenous anti‐amyloid compounds

2011· article· en· W2046293703 on OpenAlexaff
Donald F. Weaver, Autumn Meek, Felix Meier‐Stephenson, Rose Chen, Michael J. Carter

Bibliographic record

VenueAlzheimer s & Dementia · 2011
Typearticle
Languageen
FieldMedicine
TopicCholinesterase and Neurodegenerative Diseases
Canadian institutionsDalhousie University
Fundersnot available
KeywordsIn silicoEndogenyThioflavinIn vitroChemistryAmyloid (mycology)NeurotoxicityBiochemistryPeptideSmall moleculeProtein aggregationDrug discoveryComputational biologyBiologyAlzheimer's diseaseToxicity

Abstract

fetched live from OpenAlex

Since peptide sequences other than ß-amyloid are susceptible to aggregation (the “amylome”), there is evolutionary pressure to inhibit deleterious protein-misfolding; accordingly, it is reasonable to postulate the existence of endogenous anti-aggregation molecules that could arrest the neurotoxic cascade of AD. From the Aß perspective, compounds binding to the EVHHQKLVFF basic residues of Aß could in principle interrupt glycosaminoglycan/ß-amyloid interactions, inhibiting neurotoxic aggregation. Searching for an “endogenous anti-AD compound” represents an unexplored concept in AD therapeutics design. An in silico to in vitro approach was employed. We created a comprehensive in silico library of 1,450 molecules (molecular weight less than 600 g/mol) that are endogenous to the human brain. These compounds were then screened in silico, using a molecular mechanics/molecular dynamics approach, for ability to bind to the EVHHQKLVFF region of Aß. Compounds identified in this in silico screen were then experimentally evaluated using in vitro assays (ThT, CD, MTT cell viability, EM aggregation) to assess their capacity to inhibit Aß aggregation and related neurotoxicity. To identify an endogenous anti-protein-misfolding compound, we devised a computational model of non-neurotoxic, unaggregated Aß; next, we devised an in silico strategy to screen a library of endogenous compounds for molecules capable of binding Aß's HHQK/BBXB domain. This identified a family of L-phosposerine metabolites as endogenous anti-aggregants. We next performed in vitro assays to assess the capacity of these compounds to inhibit Aß aggregation. L-phosphoserine was able to block Aß aggregation in a Thioflavin-T aggregation assay (IC-50 = 3.4 micromolar); to prevent Aß conformational change from helix to ß-sheet in circular dichroism studies; to bind to Aß (at HHQK) in mass spectrometry and NMR studies; to prevent Aß aggregation in electron microscopy studies; and to prevent Aß cellular toxicity in an (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium (MTT) assay. Therefore, L-phosphoserine is an endogenous Aß anti-aggregant. An in silico screening strategy has identified a class of endogenous compounds that is able to inhibit Aß aggregation in vitro. Significantly, it is also known that L-phosphoserine neurochemistry is abnormal in AD patients; e.g. L-phosphoserine is one of the neural phosphomonoesters that is elevated in Alzheimer's disease brain.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

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 studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.150
GPT teacher head0.309
Teacher spread0.159 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2011
Admission routes1
Has abstractyes

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