Targeted cross-linking-mass spectrometry determines vicinal interactomes within heterogeneous RNP complexes
Bibliographic record
Abstract
Proteomic and RNomic approaches have identified many components of different ribonucleoprotein particles (RNPs), yet still little is known about the organization and protein proximities within these heterogeneous and highly dynamic complexes. Here we describe a targeted cross-linking approach, which combines cross-linking from a known anchor site with affinity purification and mass spectrometry (MS) to identify the changing vicinity interactomes along RNP maturation pathways. Our method confines the reaction radius of a heterobifunctional cross-linker to a specific interaction surface, increasing the probability to capture low abundance conformations and transient vicinal interactors too infrequent for identification by traditional cross-linking-MS approaches, and determine protein proximities within RNPs. Applying the method to two conserved RNA-associated complexes in Saccharomyces cerevisae, the mRNA export receptor Mex67:Mtr2 and the pre-ribosomal Nop7 subcomplex, we identified dynamic vicinal interactomes within those complexes and along their changing pathway milieu. Our results therefore show that this method provides a new tool to study the changing spatial organization of heterogeneous dynamic RNP complexes.
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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".