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Record W3111875431 · doi:10.1002/alz.045757

Epitope prediction for oligomer‐selective antibodies in tau and Aβ

2020· article· en· W3111875431 on OpenAlexaff
Steven S. Plotkin, Shawn C. C. Hsueh, Adekunle Aina, Xubiao Peng, Ebrima Gibbs, Andrei Roman, Beibei Zhao, Sarah Louadi, Johanne Kaplan, Neil R. Cashman

Bibliographic record

VenueAlzheimer s & Dementia · 2020
Typearticle
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsAmorfix (Canada)University of British Columbia
Fundersnot available
KeywordsEpitopeOligomerAntibodyFibrilChemistryLinear epitopeConformational epitopeBiophysicsIn vitroBiochemistryCell biologyBiologyImmunology

Abstract

fetched live from OpenAlex

Abstract Background Previous studies of Alzheimer’s disease (AD) pathology point to cytotoxic tau as a cause of neuronal cell death, which is induced or exacerbated by soluble misfolded Aβ oligomers. Soluble misfolded species of both tau and Aβ are both observed to propagate cell‐to‐cell. A method for identifying antibodies to tau and Aβ that are conformationally‐selective to propagative misfolded oligomeric forms, and which also have low affinity to isolated monomers or, particularly for Aβ, low affinity to fibrils, is thus a highly desired goal that holds significant promise for AD therapy. Method We have developed a novel computational platform to identify epitopes that may be selectively exposed on oligomers. Epitope prediction ideally uses an experimentally determined fibril structure as input, but the method alters this structure using molecular dynamics, to more accurately model the regions that may be exposed on soluble oligomers. Both primary sequence and structural conformation are taken into account: The epitope should be conformationally‐distinct from those conformations presented in the functional healthy protein. Epitope scaffolding is then employed to optimize the presentation of the epitope in animal immunizations, so that the resulting antibodies are predicted to be selective to misfolded oligomeric forms. Result Oligomer‐specific epitope predictions for tau and for Aβ have been used to raise preclinical antibodies that have selectivity for pathogenic tau and Aβ species. In vitro SPR measurements confirmed selective binding to synthetic oligomers and soluble pre‐formed fibrils (PFFs), vs. native healthy protein. As well, the antibodies showed little immunoreactivity toplaque or vascularAβ deposits via immunohistochemistry, while SEC fractionation of AD brain homogenate shows selective binding to toxic dimers, tetramers and dodecamers, in contrast to aducanumab and bapineuzumab. Tau antibodies recognized tau from AD brain extract, and inhibited seeding activity in a FRET assay. Aβ antibodies alleviated the cognitive deficits caused by oligomers in mouse NOR studies. Conclusion Lead antibodies to tau and Aβ developed using rationally designed conformational epitopes are likely to achieve greater therapeutic potency by selectively targeting soluble toxic oligomers, and reducing the risk of target distraction and ARIA.

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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.301
Teacher spread0.261 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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