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Record W2238621957 · doi:10.1021/acs.biochem.5b01073

Chemical Inhibitors of Epigenetic Methyllysine Reader Proteins

2015· review· en· W2238621957 on OpenAlexafffund
Natalia Milosevich, Fraser Hof

Bibliographic record

VenueBiochemistry · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsUniversity of Victoria
FundersCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsEpigeneticsBiologyDrug discoveryMethyltransferaseComputational biologyMethylationBiochemistryDNAGene

Abstract

fetched live from OpenAlex

Protein methylation is a common post-translational modification with diverse biological functions. Methyllysine reader proteins are increasingly a focus of epigenetics research and play important roles in regulating many cellular processes. These reader proteins are vital players in development, cell cycle regulation, stress responses, oncogenesis, and other disease pathways. The recent emergence of a small number of chemical inhibitors for methyllysine reader proteins supports the viability of these proteins as targets for drug development. This article introduces the biochemistry and biology of methyllysine reader proteins, provides an overview of functions for those families of readers that have been targeted to date (MBT, PHD, tudor, and chromodomains), and reviews the development of synthetic agents that directly block their methyllysine reading functions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.510
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.041
GPT teacher head0.334
Teacher spread0.293 · 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 teacher head, not a consensus.

Study designBench or experimental
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

Citations50
Published2015
Admission routes2
Has abstractyes

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