Interactome Mapping Uncovers a General Role for Numb in Protein Kinase Regulation
Bibliographic record
Abstract
Cellular functions are frequently regulated by protein-protein interactions involving the binding of a modular domain in one protein to a specific peptide sequence in another. This mechanism may be explored to identify binding partners for proteins harboring a peptide-recognition domain. Here we report a proteomic strategy combining peptide and protein microarray screening with biochemical and cellular assays to identify modular domain-mediated protein-protein interactions in a systematic manner. We applied this strategy to Numb, a multi-functional protein containing a phosphotyrosine-binding (PTB) domain. Through the screening of a protein microarray, we identified >100 protein kinases, including both Tyr and Ser/Thr kinases, that could potentially interact with the Numb PTB domain, suggesting a general role for Numb in regulating kinase function. The putative interactions between Numb and several tyrosine kinases were subsequently validated by GST pull-down and/or co-immunoprecipitation assays. Furthermore, using the Oriented Peptide Array Library approach, we defined the specificity of the Numb PTB domain which, in turn, allowed us to predict binding partners for Numb at the genome level. The combination of the protein microarray screening with computer-aided prediction produced the most expansive interactome for Numb to date, implicating Numb in regulating phosphorylation signaling through protein kinases and phosphatases. Not only does the data generated from this study provide an important resource for hypothesis-driven research to further define the function of Numb, the proteomic strategy described herein may be employed to uncover the interactome for other peptide-recognition domains whose consensus motifs are known or can be determined.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".