MétaCan
Menu
Back to cohort
Record W2210597779 · doi:10.1074/jbc.m115.680389

Structural Basis for the Discriminative Recognition of N6-Methyladenosine RNA by the Human YT521-B Homology Domain Family of Proteins

2015· article· en· W2210597779 on OpenAlexaff
Chao Xu, Ke Liu, Hazem Ahmed, P. Loppnau, Matthieu Schapira, Jinrong Min

Bibliographic record

VenueJournal of Biological Chemistry · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsStructural Genomics ConsortiumUniversity of Toronto
FundersWellcome Trust
KeywordsRNAGuanosineRNA-binding proteinBiologyBiochemistryGeneticsCell biologyGene

Abstract

fetched live from OpenAlex

N(6)-Methyladenosine (m(6)A) is the most abundant internal modification in RNA and is specifically recognized by YT521-B homology (YTH) domain-containing proteins. Recently we reported that YTHDC1 prefers guanosine and disfavors adenosine at the position preceding the m(6)A nucleotide in RNA and preferentially binds to the GG(m(6)A)C sequence. Now we systematically characterized the binding affinities of the YTH domains of three other human proteins and yeast YTH domain protein Pho92 and determined the crystal structures of the YTH domains of human YTHDF1 and yeast Pho92 in complex with a 5-mer m(6)A RNA, respectively. Our binding and structural data revealed that the YTH domain used a conserved aromatic cage to recognize m(6)A. Nevertheless, none of these YTH domains, except YTHDC1, display sequence selectivity at the position preceding the m(6)A modification. Structural comparison of these different YTH domains revealed that among those, only YTHDC1 harbors a distinctly selective binding pocket for the nucleotide preceding the m(6)A nucleotide.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0020.001

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 head0.064
GPT teacher head0.305
Teacher spread0.241 · 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 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

Citations327
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Biological ChemistrySame topicRNA modifications and cancerFrench-language works237,207