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Record W4320484431 · doi:10.1002/jcb.30380

Toward effective Atg8‐based ATTECs: Approaches and perspectives

2023· article· en· W4320484431 on OpenAlexafffund
Martin P. Schwalm, Stefan Knapp, Vladimir V. Rogov

Bibliographic record

VenueJournal of Cellular Biochemistry · 2023
Typearticle
Languageen
FieldMedicine
TopicPeptidase Inhibition and Analysis
Canadian institutionsStructural Genomics Consortium
FundersOntario Genomics InstituteEuropean Federation of Pharmaceutical Industries and AssociationsMerck KGaAOntario GenomicsGenome CanadaMcGill UniversityGenentechBayerPfizerDeutsche ForschungsgemeinschaftBristol-Myers Squibb
KeywordsATG8Computational biologyIdentification (biology)ChemistryBiologyAutophagyBiochemistryEcology

Abstract

fetched live from OpenAlex

Induction of Atg8-family protein (LC3/GABARAP proteins in human) interactions with target proteins of interest by proximity-inducing small molecules offers the possibility for novel targeted protein degradation approaches. However, despite intensive screening campaigns during the last 5 years, no potent ligands for LC3/GABARAPs have been developed, rendering this approach largely unexplored and unsuitable for therapeutic exploitation. In this Viewpoint, we analyze the reported attempts identifying LC3/GABARAP inhibitors and provide our own point of view why no potent inhibitors have been found. Additionally, we designate reasonable directions for the identification of potent and probably selective LC3/GABARAP inhibitors for alternative therapeutic applications.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.039
GPT teacher head0.256
Teacher spread0.217 · 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 designBench or experimental
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

Citations7
Published2023
Admission routes2
Has abstractyes

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