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Record W2992595646 · doi:10.3390/molecules24244492

Methyltransferase Inhibitors: Competing with, or Exploiting the Bound Cofactor

2019· review· en· W2992595646 on OpenAlexafffund
Renato Ferreira de Freitas, Danton Ivanochko, Matthieu Schapira

Bibliographic record

VenueMolecules · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related gene regulation
Canadian institutionsCanada Research ChairsStructural Genomics ConsortiumPrincess Margaret Cancer CentreUniversity of Toronto
FundersNovartis PharmaFundação de Amparo à Pesquisa do Estado de São PauloOntario Genomics InstituteEuropean Federation of Pharmaceutical Industries and AssociationsMerck KGaAGenome CanadaOntario GenomicsNatural Sciences and Engineering Research Council of CanadaPfizer
KeywordsDruggabilityCofactorMethyltransferaseBinding siteChemical biologyEnzymeChemistryTransferaseBiochemistryActive siteDrug discoveryPlasma protein bindingStereochemistryBiologyDNAMethylation

Abstract

fetched live from OpenAlex

Protein methyltransferases (PMTs) are enzymes involved in epigenetic mechanisms, DNA repair, and other cellular machineries critical to cellular identity and function, and are an important target class in chemical biology and drug discovery. Central to the enzymatic reaction is the transfer of a methyl group from the cofactor S-adenosylmethionine (SAM) to a substrate protein. Here we review how the essentiality of SAM for catalysis is exploited by chemical inhibitors. Occupying the cofactor binding pocket to compete with SAM can be hindered by the hydrophilic nature of this site, but structural studies of compounds now in the clinic revealed that inhibitors could either occupy juxtaposed pockets to overlap minimally, but sufficiently with the bound cofactor, or induce large conformational remodeling leading to a more druggable binding site. Rather than competing with the cofactor, other inhibitors compete with the substrate and rely on bound SAM, either to allosterically stabilize the substrate binding site, or for direct SAM-inhibitor interactions.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.042
GPT teacher head0.309
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 teacher head, not a consensus.

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

Citations60
Published2019
Admission routes2
Has abstractyes

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