Muscleblind-like proteins use modular domains to localize RNAs by riding kinesins and docking to membranes
Bibliographic record
Abstract
RNA binding proteins (RBPs) act as critical facilitators of spatially regulated gene expression. Muscleblind-like (MBNL) proteins, implicated in myotonic dystrophy and cancer, localize RNAs to myoblast membranes and neurites through unknown mechanisms. We find that MBNL forms motile and anchored granules in neurons and myoblasts, and selectively associates with kinesins Kif1bα and Kif1c through its zinc finger (ZnF) domains. Other RBPs with similar ZnFs associate with these kinesins, implicating a motor-RBP specificity code. MBNL and kinesin perturbation leads to widespread mRNA mis-localization, including depletion of Nucleolin transcripts from neurites. Live cell imaging and fractionation reveal that the unstructured carboxy-terminal tail of MBNL1 allows for anchoring at membranes. An approach, termed RBP Module Recruitment and Imaging (RBP-MRI), reconstitutes kinesin- and membrane-recruitment functions using MBNL-MS2 coat protein fusions. Our findings decouple kinesin association, RNA binding, and membrane anchoring functions of MBNL while establishing general strategies for studying multi-functional, modular domains of RBPs.
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 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".