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Record W4386475323 · doi:10.1101/gad.351120.123

Advancing therapeutics using antibody-induced dimerization of receptor tyrosine phosphatases

2023· letter· en· W4386475323 on OpenAlexafffund
Michel L. Tremblay

Bibliographic record

VenueGenes & Development · 2023
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Tyrosine Phosphatases
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsProtein tyrosine phosphataseBiologyPhosphataseExtracellularPhosphorylationIntracellularProteolysisTyrosineCell biologyReceptor tyrosine kinaseReceptorImmunoprecipitationMonoclonal antibodyCancer researchGeneAntibodyEnzymeBiochemistryGenetics

Abstract

fetched live from OpenAlex

Receptor protein tyrosine phosphatases (RPTPs) are involved in a broad list of cellular, developmental, and physiological functions. Altering their expression leads to significant changes in protein phosphorylation linked to a growing list of human diseases, including cancers and neurological disorders. In this issue ofGenes & Development, Qian and colleagues (pp. 743–759) present the identification of a monoclonal antibody targeting PTPRD extracellular domain-inducing dimerization and inhibition of the phosphatase activities, causing the proteolysis of dimeric PTPRD by a mechanism involving intracellular degradation pathways. Their study supports the potential of modulating PTPRD via its extracellular domains. This opens a new framework in the clinical manipulation of PTPRD and its closely related family members.

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)0.002
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0110.011
Insufficient payload (model declined to judge)0.0040.004

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 head0.027
GPT teacher head0.294
Teacher spread0.267 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

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