Identification of protein‐protein interactions using <b><i>in vivo</i></b> cross‐linking and mass spectrometry
Bibliographic record
Abstract
The purification of protein complexes can be accomplished by different types of affinity chromatography. In a typical immunoaffinity experiment, protein complexes are captured from a cell lysate by an immobilized antibody that recognizes an epitope on one of the known components of the complex. After extensive washing to remove unspecifically bound proteins, the complexes are eluted and analyzed by mass spectrometry (MS). Transient complexes, which are characterized by high dissociation constants, are typically lost by this approach. In the present study, we describe a novel method for identifying transient protein-protein interactions using in vivo cross-linking and MS-based protein identification. Live cells are treated with formaldehyde, which rapidly permeates the cell membrane and generates protein-protein cross-links. Proteins cross-linked to a Myc-tagged protein of interest are copurified by immunoaffinity chromatography and subjected to a procedure which dissociates the cross-linked complexes. After separation by SDS-PAGE, proteins are identified by tandem mass spectrometry. Application of this method enabled the identification of numerous proteins that copurified with a constitutively active form of M-Ras (M-Ras(Q71L)). Among these, we identified the RasGAP-related protein IQGAP1 to be a novel interaction partner of M-Ras(Q71L). This method is applicable to many proteins and will aid in the study of protein-protein interactions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".