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Record W2013770157 · doi:10.1021/ci300596x

Strategy to Target the Substrate Binding site of SET Domain Protein Methyltransferases

2013· article· en· W2013770157 on OpenAlexafffund
Kong T. Nguyen, Fengling Li, Gennadiy Poda, David Smil, Masoud Vedadi, Matthieu Schapira

Bibliographic record

VenueJournal of Chemical Information and Modeling · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related gene regulation
Canadian institutionsCanada Research ChairsUniversity of New BrunswickStructural Genomics ConsortiumOntario Institute for Cancer ResearchUniversity of Toronto
FundersMinistero dello Sviluppo EconomicoGovernment of OntarioEli Lilly CanadaWellcome TrustEli Lilly and Company
KeywordsMethyltransferaseLysineComputational biologyChromatinMethylationDomain (mathematical analysis)Substrate (aquarium)ChemistryBiochemistryBiologyGeneAmino acid

Abstract

fetched live from OpenAlex

Protein methyltransferases (PMTs) are a novel gene family of therapeutic relevance involved in chromatin-mediated signaling and other biological mechanisms. Most PMTs are organized around the structurally conserved SET domain that catalyzes the methylation of a substrate lysine. A few potent chemical inhibitors compete with the protein substrate, and all are anchored in the channel recruiting the methyl-accepting lysine. We propose a novel strategy to design focused chemical libraries targeting the substrate binding site, where a limited number of warheads each occupying the lysine-channel of multiple enzymes would be decorated by different substituents. A variety of sequence and structure-based approaches used to analyze the diversity of the lysine channel of SET domain PMTs support the relevance of this strategy. We show that chemical fragments derived from published inhibitors are valid warheads that can be used in the design of novel focused libraries targeting other PMTs.

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 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: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.200

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.015
GPT teacher head0.251
Teacher spread0.237 · 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.

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

Citations16
Published2013
Admission routes2
Has abstractyes

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