Coupling substrate-trapping with proximity-labeling to identify protein tyrosine phosphatase PTP1B signaling networks
Bibliographic record
Abstract
The ability to define functional interactions between enzymes and their substrates is crucial for understanding biological control mechanisms; however, such methods face challenges in the transient nature and low stoichiometry of enzyme-substrate interactions. Now, we have developed an optimized strategy that couples substrate-trapping mutagenesis to proximity-labeling mass spectrometry for quantitative analysis of protein complexes involving the protein tyrosine phosphatase PTP1B. This methodology represents a significant shift from classical schemes; it is capable of being performed at near-endogenous expression levels and increasing stoichiometry of target enrichment without a requirement for stimulation of supraphysiological tyrosine phosphorylation levels or maintenance of substrate complexes during lysis and enrichment procedures. Advantages of this new approach are illustrated through application to PTP1B interaction networks in models of HER2-positive and Herceptin-resistant breast cancer. We have demonstrated that inhibitors of PTP1B significantly reduced proliferation and viability in cell-based models of acquired and de novo Herceptin resistance in HER2-positive breast cancer. Using differential analysis, comparing substrate-trapping to wild-type PTP1B, we have identified multiple unreported protein targets of PTP1B with established links to HER2-induced signaling and provided internal validation of method specificity through overlap with previously identified substrate candidates. Overall, this versatile approach can be readily integrated with evolving proximity-labeling platforms (TurboID, BioID2, etc.), and is broadly applicable across all PTP family members for the identification of conditional substrate specificities and signaling nodes in models of human disease.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".