Discovery of Cell Compartment Specific Protein–Protein Interactions using Affinity Purification Combined with Tandem Mass Spectrometry
Bibliographic record
Abstract
Affinity purification combined with tandem mass spectrometry (AP-MS/MS) is a well-established method used to discover interaction partners for a given protein of interest. Because most AP-MS/MS approaches are performed using the soluble fraction of whole cell extracts (WCE), information about the cellular compartments where the interactions occur is lost. More importantly, classical AP-MS/MS often fails to identify interactions that take place in the nonsoluble fraction of the cell, for example, on the chromatin or membranes; consequently, protein complexes that are less soluble are underrepresented. In this paper, we introduce a method called multiple cell compartment AP-MS/MS (MCC-AP-MS/MS), which identifies the interactions of a protein independently in three fractions of the cell: the cytoplasm, the nucleoplasm, and the chromatin. We show that this fractionation improves the sensitivity of the method when compared to the classical affinity purification procedure using soluble WCE while keeping a very high specificity. Using three proteins known to localize in various cell compartments as baits, the CDK9 subunit of transcription elongation factor P-TEFb, the RNA polymerase II (RNAP II)-associated protein 4 (RPAP4), and the largest subunit of RNAP II, POLR2A, we show that MCC-AP-MS/MS reproducibly yields fraction-specific interactions. Finally, we demonstrate that this improvement in sensitivity leads to the discovery of novel interactions of RNAP II carboxyl-terminal domain (CTD) interacting domain (CID) proteins with POLR2A.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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) | 0.000 | 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 teacher head, 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".