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Record W2600126358 · doi:10.1101/cshperspect.a024067

Structural and Chemical Biology of Presenilin Complexes

2017· review· en· W2600126358 on OpenAlexafffund
Douglas S. Johnson, Yue‐Ming Li, Martin Pettersson, Peter St George‐Hyslop

Bibliographic record

VenueCold Spring Harbor Perspectives in Medicine · 2017
Typereview
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsUniversity of Toronto
FundersMedical Research CouncilCanadian Institutes of Health ResearchNational Institutes of HealthAlzheimer SocietyWellcome Trust
KeywordsPresenilinAllosteric regulationNicastrinStructural biologyTransmembrane proteinTransmembrane domainChemistryActive siteProteasesBiochemistryEnzymeStereochemistryBiophysicsBiologyMembraneReceptor

Abstract

fetched live from OpenAlex

The presenilin proteins are the catalytic subunits of a tetrameric complex containing presenilin 1 or 2, anterior pharynx defective 1 (APH1), nicastrin, and PEN-2. Other components such as TMP21 may exist in a subset of specialized complexes. The presenilin complex is the founding member of a unique class of aspartyl proteases that catalyze the γ, ɛ, ζ site cleavage of the transmembrane domains of Type I membrane proteins including amyloid precursor protein (APP) and Notch. Here, we detail the structural and chemical biology of this unusual enzyme. Taken together, these studies suggest that the complex exists in several conformations, and subtle long-range (allosteric) shifts in the conformation of the complex underpin substrate access to the catalytic site and the mechanism of action for allosteric inhibitors and modulators. Understanding the mechanics of these shifts will facilitate the design of γ-secretase modulator (GSM) compounds that modulate the relative efficiency of γ, ɛ, ζ site cleavage and/or substrate specificity.

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: Review · Consensus signal: Review
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)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.131
GPT teacher head0.441
Teacher spread0.310 · 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
GenreReview

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

Citations18
Published2017
Admission routes2
Has abstractyes

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