Extracellular proximal interaction profiling by cell surface–targeted TurboID reveals LDLR as a partner of liganded EGFR
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Plasma membrane proteins play pivotal roles in receiving and transducing signals from other cells and from the environment and are vital for cellular functionality. Enzyme-based, proximity-dependent approaches, such as biotin identification (BioID), combined with mass spectrometry have begun to illuminate the landscape of proximal protein interactions within intracellular compartments. To extend the potential of these approaches to study the extracellular environment, we developed extracellular TurboID (ecTurboID), a method designed to profile the interactions between proteins on the surfaces of living cells over short timescales using the fast-acting biotin ligase TurboID. After optimizing our experimental and data analysis strategies to capture extracellular proximity interactions, we used ecTurboID to reveal the proximal interactomes of several plasma membrane proteins, including the epidermal growth factor receptor (EGFR). We found that EGF stimulation induced an association between EGFR and the low-density lipoprotein receptor (LDLR) and changed the interactome of LDLR by increasing its proximity with proteins that regulate EGFR signaling. The identification of this interaction between two well-studied and clinically relevant receptors illustrates the utility of our modified proximity labeling methodology for identifying dynamic extracellular associations between plasma membrane proteins.
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.
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 | 0.000 |
| 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 it