Photoaffinity-engineered protein scaffold for systematically exploring native phosphotyrosine signaling complexes in tumor samples
Bibliographic record
Abstract
Phosphotyrosine (pTyr)-regulated protein complexes play critical roles in cancer signaling. The systematic characterization of these protein complexes in tumor samples remains a challenge due to their limited access and the transient nature of pTyr-mediated interactions. We developed a hybrid chemical proteomics approach, termed Photo-pTyr-scaffold, by engineering Src homology 2 (SH2) domains, which specifically bind pTyr proteins, with both trifunctional chemical probes and genetic mutations to overcome these challenges. Dynamic SH2 domain-scaffolding protein complexes were efficiently cross-linked under mild UV light, captured by biotin tag, and identified by mass spectrometry. This approach was successfully used to profile native pTyr protein complexes from breast cancer tissue samples on a proteome scale with high selectivity, achieving about 100 times higher sensitivity for detecting pTyr signaling proteins than that afforded by traditional immunohistochemical methods. Among more than 1,000 identified pTyr proteins, receptor tyrosine kinase PDGFRB expressed on cancer-associated fibroblasts was validated as an important intercellular signaling regulator with poor expression correlation to ERBB2, and blockade of PDGFRB signaling could efficiently suppress tumor growth. The Photo-pTyr-scaffold approach may become a generic tool for readily profiling dynamic pTyr signaling complexes in clinically relevant samples.
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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".