MétaCan
Menu
Back to cohort
Record W2594378769 · doi:10.1073/pnas.1612444114

Structure of human IFIT1 with capped RNA reveals adaptable mRNA binding and mechanisms for sensing N1 and N2 ribose 2′-O methylations

2017· article· en· W2594378769 on OpenAlexafffund
Y.M. Abbas, Beatrice T. Laudenbach, Saúl Martínez‐Montero, Regina Cencic, Matthias Habjan, Andreas Pichlmair, Masad J. Damha, Jerry Pelletier, Bhushan Nagar

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsOccupational Cancer Research CentreMcGill University
FundersCanadian Institutes of Health Research
KeywordsMessenger RNATranslation (biology)BiologyNucleotideRNAEffectorComputational biologyInnate immune systemFlexibility (engineering)Cell biologyNucleic acidGeneticsImmune systemGene

Abstract

fetched live from OpenAlex

) giving rise to a relatively plastic and nonspecific mode of binding, in stark contrast to eIF4E. Cap-proximal nucleotides encircled by the tunnel provide affinity to compete with eIF4F while allowing IFIT1 to select against N1 methylated mRNA. Gel-shift binding assays confirm that N1 methylation interferes with IFIT1 binding, but in an RNA-dependent manner, whereas translation assays reveal that N1 methylation alone is not sufficient to prevent mRNA recognition at high IFIT1 concentrations. Structural and functional analysis show that 2'-O methylation at N2, another abundant mRNA modification, is also detrimental for RNA binding, thus revealing a potentially synergistic role for it in self- versus nonself-mRNA discernment. Finally, structure-guided mutational analysis confirms the importance of RNA binding for IFIT1 restriction of a human coronavirus mutant lacking viral N1 methylation. Our structural and biochemical analysis sheds new light on the molecular basis for IFIT1 translational inhibition of capped viral RNA.

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 score0.029
Threshold uncertainty score0.377

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 head0.043
GPT teacher head0.324
Teacher spread0.281 · 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

Citations136
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicRNA modifications and cancerFrench-language works237,207