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Record W4416337617 · doi:10.1038/s41589-025-02076-y

Temporal photoproximity labeling of ligand-activated EGFR neighborhoods using MultiMap

2025· article· en· W4416337617 on OpenAlexfundno aff
Zhi Lin, Wayne Ngo, Yu‐Ting Chou, Harry X. Wu, Katherine J. Susa, Young‐wook Jun, Trever G. Bivona, Jennifer A. Doudna, James A. Wells

Bibliographic record

VenueNature Chemical Biology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiotin and Related Studies
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesUniversity of California, San FranciscoNational Institutes of HealthNatural Sciences and Engineering Research Council of CanadaJames B. Pendleton Charitable TrustGladstone Institutes
KeywordsInteractomeWorkflowProteomicsEpidermal growth factor receptorSignal transductionCell signalingSet (abstract data type)Cell

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.299
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2025
Admission routes1
Has abstractyes

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