Temporal photoproximity labeling of ligand-activated EGFR neighborhoods using MultiMap
Bibliographic record
Abstract
Photoproximity labeling proteomics (PLP) methods have recently shown that cell surface receptors can form lateral interactome networks. Here, we present a paired set of PLP workflows that dynamically track neighborhood changes for oncogenic epidermal growth factor receptor (EGFR) over time, both outside and inside of cells. We achieved this by augmenting the multiscale PLP workflow we call MultiMap, where three photoprobes with different labeling ranges were photoactivated by one photocatalyst, eosin Y, anchored extracellularly and intracellularly on EGFR. We identified hundreds of neighboring proteins that changed within minutes to over 1 h after the addition of EGF. These neighborhoods reveal dynamic interactomes during early, middle and late signaling that drive phosphorylation, internalization, degradation and transcriptional regulation. This rapid 'molecular photographic' labeling approach provides snapshots of signaling neighborhoods, revealing their dynamic nature and potential for drug targeting.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| 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".