Phosphotyrosine signaling: a prototype for modular protein‐protein interactions
Bibliographic record
Abstract
Signal transduction pathways are typically controlled by regulated protein‐protein interactions, mediated by dedicated interaction domains. The prototype for such interactions involves the recognition of phosphotyrosine sites on receptor tyrosine kinases by the SH2 domains of cytoplasmic signaling proteins. Many other types of post‐translational modifications are also recognized by specific interaction domains, which therefore provide a general mechanism to couple the dynamic state of the proteome to cellular responses. There are ~100 classes of interaction domains present in human proteins, with each class being represented by up to 300 members. Interaction domains therefore represent a prevalent feature of the proteome. I will argue that they provide a simple mechanism for the evolution of new biological functions, and conversely that aberrant protein‐protein interactions are a common basis for disease. Adaptor proteins are composed exclusively of interaction sequences, and serve to couple signaling receptors to specific components of the core cellular machinery, thereby shaping the cellular response to a particular biological input. I will discuss the ability of adaptors to control cellular behaviour, and the mechanisms by which pathogenic proteins can exploit this molecular device to re‐wire cellular function.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".