Synthetic antibodies as tools to probe RNA-binding protein function
Bibliographic record
Abstract
RNA-binding proteins (RBPs) have essential roles in post-transcriptional regulation of gene expression. They bind sequence elements in specific mRNAs and control their splicing, transport, localization, translation, and stability. A complete understanding of RBP function requires identification of the target RNAs that an RBP regulates, the mechanisms by which the RBP regulates these targets, and the biological consequences for the cell in which these transactions occur. Antibodies are key tools in such studies: first, mRNA targets of RBPs can be identified by co-immunoprecipitation of RBPs with their associated RNAs followed by microarray analysis or sequencing; second, partner proteins can be identified by immunoprecipitation of the RBP followed by mass spectrometry; third, the mechanisms and functions of RBPs can be inferred from loss-of-function studies employing antibodies that block RBP-RNA interactions. One potentially powerful approach to making antibodies for such studies is the generation of synthetic antibodies using phage display, which involves in vitro selection using a human-designed antibody library to generate antibodies that recognize a target protein. Using two well-characterized Drosophila RNA-binding proteins, Staufen and Smaug, for proof-of-principle, we demonstrate that synthetic antibodies can be generated and used either to perform RNA-coimmunoprecipitations (RIPs) to identify RBP-bound mRNAs, or to block RBP-RNA interactions. Given that synthetic antibody selection protocols are amenable to high-throughput antibody production, these results demonstrate that synthetic antibodies can be powerful tools for genome-wide studies of RBP function.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".