Binding specificities of human RNA binding proteins towards structured and linear RNA sequences
Bibliographic record
Abstract
ABSTRACT Sequence specific RNA-binding proteins (RBPs) control many important processes affecting gene expression. They regulate RNA metabolism at multiple levels, by affecting splicing of nascent transcripts, RNA folding, base modification, transport, localization, translation and stability. Despite their central role in most aspects of RNA metabolism and function, most RBP binding specificities remain unknown or incompletely defined. To address this, we have assembled a genome-scale collection of RBPs and their RNA binding domains (RBDs), and assessed their specificities using high throughput RNA-SELEX (HTR-SELEX). Approximately 70% of RBPs for which we obtained a motif bound to short linear sequences, whereas ~30% preferred structured motifs folding into stem-loops. We also found that many RBPs can bind to multiple distinctly different motifs. Analysis of the matches of the motifs in human genomic sequences suggested novel roles for many RBPs. We found that three cytoplasmic proteins, ZC3H12A, ZC3H12B and ZC3H12C bound to motifs resembling the splice donor sequence, suggesting that these proteins are involved in degradation of cytoplasmic viral and/or unspliced transcripts. Surprisingly, structural analysis revealed that the RNA motif was not bound by the conventional C3H1 RNA-binding domain of ZC3H12B. Instead, the RNA motif was bound by the ZC3H12B’s PilT N-terminus (PIN) RNase domain, revealing a potential mechanism by which unconventional RNA binding domains containing active sites or molecule-binding pockets could interact with short, structured RNA molecules. Our collection containing 145 high resolution binding specificity models for 86 RBPs is the largest systematic resource for the analysis of human RBPs, and will greatly facilitate future analysis of the various biological roles of this important class of proteins.
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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) | 0.002 | 0.001 |
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.
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".