MétaCan
Menu
Back to cohort
Record W4246425171 · doi:10.7554/elife.43676

Structural insights into SETD3-mediated histidine methylation on β-actin

2019· article· en· W4246425171 on OpenAlexaff
Qiong Guo, Shanhui Liao, S. Kwiatkowski, Weronika Tomaka, Huijuan Yu, Gao Wu, Xiaoming Tu, Jinrong Min, Jakub Drożak, Chao Xu

Bibliographic record

VenueeLife · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related gene regulation
Canadian institutionsStructural Genomics ConsortiumUniversity of Toronto
FundersFundamental Research Funds for the Central UniversitiesNarodowe Centrum NaukiChina Postdoctoral Science FoundationWellcome TrustUniversity of Science and Technology of ChinaNational Natural Science Foundation of China
KeywordsMethylationMethyltransferaseActinHistidineEnhancerBiologyBiochemistryChemistryCell biologyEnzymeGene

Abstract

fetched live from OpenAlex

SETD3 is a member of the SET (Su(var)3–9, Enhancer of zeste, and Trithorax) domain protein superfamily and plays important roles in hypoxic pulmonary hypertension, muscle differentiation, and carcinogenesis. Previously, we identified SETD3 as the actin-specific methyltransferase that methylates the N3 of His73 on β-actin (Kwiatkowski et al., 2018). Here, we present two structures of S-adenosyl-L-homocysteine-bound SETD3 in complex with either an unmodified β-actin peptide or its His-methylated variant. Structural analyses, supported by biochemical experiments and enzyme activity assays, indicate that the recognition and methylation of β-actin by SETD3 are highly sequence specific, and that both SETD3 and β-actin adopt pronounced conformational changes upon binding to each other. In conclusion, this study is the first to show a catalytic mechanism of SETD3-mediated histidine methylation on β-actin, which not only throws light on the protein histidine methylation phenomenon but also facilitates the design of small molecule inhibitors of SETD3.

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 score0.123
Threshold uncertainty score0.472

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 head0.007
GPT teacher head0.243
Teacher spread0.236 · 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

Citations67
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueeLifeSame topicCancer-related gene regulationFrench-language works237,207