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Record W3111139148 · doi:10.1002/alz.044674

The in silico search for endogenous anti‐Alzheimer's compounds

2020· article· en· W3111139148 on OpenAlexaff
Donald F. Weaver, Christopher Barden, Mayuri Gupta, Autumn Meek, Mark A. Reed, Yanfei Wang, Fan Wu

Bibliographic record

VenueAlzheimer s & Dementia · 2020
Typearticle
Languageen
FieldMedicine
TopicCholinesterase and Neurodegenerative Diseases
Canadian institutionsTreventis (Canada)Toronto Western HospitalKrembil Foundation
Fundersnot available
KeywordsIn silicoEndogenyPeptideChemistryComputational biologyDrug discoveryDocking (animal)Small moleculeHuman brainBiochemistryBiologyNeuroscienceMedicine

Abstract

fetched live from OpenAlex

Abstract Background Since many peptide and proteins are susceptible to oligomerization analogous to Aβ and tau, there is evolutionary pressure to inhibit deleterious protein misfolding; likewise, the immunoinflammatory cascade triggered by such misfolding is also subject to homeostatic regulation. Accordingly, it is reasonable to postulate the existence of endogenous molecules within the human brain that could modulate or even interrupt the neurotoxic cascade of AD by blocking both the proteopathy and immunopathy of Alzheimer's disease (AD). Such compounds would constitute platforms for future drug development. Method We sought to identify a single anti‐proteopathic and anti‐immunopathic agent endogenous to the human central nervous system; to find this compound, we created a comprehensive library of 1,376 molecules (molecular weight < 600 Da) naturally occurring within the human brain and employed an in silico screening assay. Using computer‐aided screening with a molecular mechanics force field, we docked these endogenous molecules against computer models of in Aβ(HHQK16LVFF), tau(KKAK144), IL‐1R1(HKEK80), IL‐1β(KLRK76), C1qA(KKGH225), IFN‐gamma(KKKR112) and RANTES(RKNR70). Additional molecular dynamics simulations were done to refine the docking. Finally, multiple in vitro assays were done, verifying that the in silico hits had correctly predicted the ability to block oligomerization and to bind to multiple immunopeptides. Result Several zwitterionic and aromatic‐anionic compounds capable of binding to these multiple amyloid, tau and immunoprotein targets were identified. Strong in silico hits included 2‐aminoethanesulfonic acid, L‐phosphoserine, 5‐hydroxytryptamine and 3‐hydroxyanthranilic acid. These predictions were verified using in vitro assays, including the kinetic Thioflavin T [ThT] aggregation assay. Conclusion Searching for an “endogenous anti‐AD compound” represents an unexplored concept. Our in silico and in vitro studies suggest that compounds endogenous to the human brain can inhibit pathological both the proteopathic and immunopathic pathogeneses of AD. The value of a novel in silico screening assay to identify such endogenous agent capable of "one‐drug‐multiple‐targets" has also been demonstrated.

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)0.001
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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.119
GPT teacher head0.327
Teacher spread0.207 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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