Purification and Identification of Protein-Tyrosine Kinase-binding Proteins Using Synthetic Phosphopeptides as Affinity Reagents
Bibliographic record
Abstract
Protein-tyrosine kinases are known regulators of cell division that have been implicated in the onset of a variety of malignancies. They act through cellular signaling proteins that bind to specific autophosphorylation sites. To find out whether these autophosphorylation sites can be used to identify downstream signaling proteins, synthetic peptides based on an autophosphorylation site in the colony-stimulating factor-1 (CSF-1) receptor were linked to agarose beads and incubated with lysates from macrophages. Bound proteins were analyzed by MS, leading to the identification of both known and novel CSF-1 receptor-interacting proteins. The approach presented here can be applied to phosphorylation sites in a wide variety of proteins. It will lead to the identification of novel protein-protein interactions and provide new insights into the mechanics of signal transduction. Novel protein-protein interactions may provide useful targets for the development of drugs that interfere with the activation of signaling cascades used by protein-tyrosine kinases to turn on cell division.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".