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Record W4214919916 · doi:10.1016/j.ymeth.2022.03.002

Structure-function analysis of DEAD-box helicase DDX43

2022· article· en· W4214919916 on OpenAlexafffund
Ravi Shankar Singh, Ananna Bhadra Arna, He Dong, Manisha Yadav, Aanchal Aggarwal, Yuliang Wu

Bibliographic record

VenueMethods · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHelicaseRNA Helicase AProcessivityDEAD boxBiologyCell biologyChromatin immunoprecipitationElectrophoretic mobility shift assayImmunoprecipitationAAA proteinsMolecular biologyChemistryDNABiochemistryGeneATPaseRNATranscription factorDNA replicationGene expressionPromoterEnzyme

Abstract

fetched live from OpenAlex

DDX43 (DEAD-box helicase 43), also known as HAGE (helicase antigen gene), is a member of the DEAD-box protein family. It contains a K homology (KH) domain in its N terminus, a helicase core domain in its C terminus, and a flexible linker domain in between. DDX43 expression is low or undetectable in normal tissue, but is overexpressed in many tumors; therefore, it is considered a potential target molecule for cancer therapy. We, along with other groups, have shown that DDX43 is an ATP-dependent RNA and DNA helicase, and the KH domain is required for its ATPase and unwinding activity. Electrophoretic mobility shift assay (EMSA), SELEX (systematic evolution of ligands by exponential enrichment), chromatin immunoprecipitation (ChIP)-seq, crosslinking immunoprecipitation (CLIP)-seq, and nuclear magnetic resonance (NMR) showed that the KH domain prefers to bind pyrimidine-rich ssDNA and ssRNA, such as TTGT in the promoter regions of genes. Moreover, the KH domain facilitates the substrate specificity and processivity of the DDX43 helicase. No animal model has been generated for DDX43; cellular studies have revealed that DDX43 has roles in piRNA amplification, tumorigenesis, RAS signaling, and innate immunity. Structural and functional studies of DDX43 will not only advance our understanding of DEAD-box helicases and KH domains, but also shed light on the application of DDX43 as therapeutics, where its key binding sites can be targeted by small molecules and natural products as an alternative approach in treating DDX43 overexpressed cancers.

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)0.001
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 score0.088
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.022
GPT teacher head0.381
Teacher spread0.359 · 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

Citations12
Published2022
Admission routes2
Has abstractyes

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