Discovery of RNA-Protein Molecular Clamps Using Proteome-Wide Stability Assays
Bibliographic record
Abstract
Uncompetitive inhibition is an effective strategy for suppressing dysregulated enzymes and their substrates, but discovery of suitable ligands depends on often-unavailable structural knowledge and serendipity. Hence, despite surging interest in mass spectrometry-based target identification, proteomic studies of substrate-dependent target engagement remain sparse. Herein, we describe a strategy for the discovery of substrate-dependent ligand binding. Using proteome integral solubility alteration (PISA) assays, we show that simple biochemical additives can enable detection of RNA-protein-small molecule complexes in native cell lysates. We apply our approach to rocaglates, molecules that specifically clamp RNA to eukaryotic translation initiation factor 4A (eIF4A), DEAD-box helicase 3X (DDX3X), and potentially other members of the DEAD-box (DDX) helicase family. To identify unexpected interactions, we used a target class-specific thermal window and compared ATP analog and RNA base dependencies for key rocaglate-DDX interactions. We report novel DDX targets of high-profile rocaglates-including the clinical candidate Zotatifin-and validate our findings using limited proteolysis-mass spectrometry and fluorescence polarization (FP) experiments. We also provide structural insight into divergent DDX3X affinities between synthetic rocaglates. Taken together, our study provides a model for screening uncompetitive inhibitors using a chemical proteomics approach and uncovers actionable DDX clamping targets, clearing a path toward characterization of novel molecular clamps and associated RNA helicases.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".