The KH domain facilitates the substrate specificity and unwinding processivity of DDX43 helicase
Bibliographic record
Abstract
The K-homology (KH) domain is a nucleic acid–binding domain present in many proteins. Recently, we found that the DEAD-box helicase DDX43 contains a KH domain in its N-terminus; however, its function remains unknown. Here, we purified recombinant DDX43 KH domain protein and found that it prefers binding ssDNA and ssRNA. Electrophoretic mobility shift assay and NMR revealed that the KH domain favors pyrimidines over purines. Mutational analysis showed that the GXXG loop in the KH domain is involved in pyrimidine binding. Moreover, we found that an alanine residue adjacent to the GXXG loop is critical for binding. Systematic evolution of ligands by exponential enrichment, chromatin immunoprecipitation–seq, and cross-linking immunoprecipitation–seq showed that the KH domain binds C-/T-rich DNA and U-rich RNA. Bioinformatics analysis suggested that the KH domain prefers to bind promoters. Using 15 N-heteronuclear single quantum coherence NMR, the optimal binding sequence was identified as TTGT. Finally, we found that the full-length DDX43 helicase prefers DNA or RNA substrates with TTGT or UUGU single-stranded tails and that the KH domain is critically important for sequence specificity and unwinding processivity. Collectively, our results demonstrated that the KH domain facilitates the substrate specificity and processivity of the DDX43 helicase.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".