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Record W2584365152 · doi:10.1002/pro.3129

Targeting human SET1/MLL family of proteins

2017· review· en· W2584365152 on OpenAlexafffund
Masoud Vedadi, Levi L. Blazer, Mohammad S. Eram, Dalia Baršytė-Lovejoy, C.H. Arrowsmith, Taraneh Hajian

Bibliographic record

VenueProtein Science · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
FundersMinistero dello Sviluppo EconomicoFundação de Amparo à Pesquisa do Estado de São PauloOntario Ministry of Economic Development and InnovationUniversity of OttawaEuropean Federation of Pharmaceutical Industries and AssociationsNovartis PharmaWellcome TrustGenome CanadaPfizer
KeywordsMethyltransferaseBiologyComputational biologyGeneticsProtein familyPlasma protein bindingBiochemistryGeneMethylation

Abstract

fetched live from OpenAlex

The SET1 family of proteins, and in particular MLL1, are essential regulators of transcription and key mediators of normal development and disease. Here, we summarize the detailed characterization of the methyltransferase activity of SET1 complexes and the role of the key subunits, WDR5, RbBP5, ASH2L, and DPY30. We present new data on full kinetic characterization of human MLL1, MLL3, SET1A, and SET1B trimeric, tetrameric, and pentameric complexes to elaborate on substrate specificities and compare our findings with what has been reported before. We also review exciting recent work identifying potent inhibitors of oncogenic MLL1 function through disruption of protein-protein interactions within the MLL1 complex.

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 integrity0.0000.001
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 head0.110
GPT teacher head0.412
Teacher spread0.302 · 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

Citations70
Published2017
Admission routes2
Has abstractyes

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