Cloning and Characterization of PHIP, a Novel Insulin Receptor Substrate-1 Pleckstrin Homology DomainInteracting Protein
Bibliographic record
Abstract
Insulin receptor substrate-1 (IRS-1) protein is a major substrate of the insulin receptor tyrosine kinase and is essential for transducing many of the biological effects of insulin including mitogenesis, gene expression, and glucose transport. The N terminus of IRS-1 contains a pleckstrin homology (PH) domain that is critical for recognition and subsequent phosphorylation of IRS-1 by the activated insulin receptor. Here we report the isolation of a novel protein, PHIP (PH-interacting protein), which selectively binds to the PH domain of IRS-1 in vitro and stably associates with IRS-1 in vivo. Importantly, mutants of the IRS-1 PH domain that disrupt the PH fold fail to bind to PHIP. Anti-phosphotyrosine immunoblots of PHIP revealed no discernible insulin receptor-regulated phosphorylation, suggesting that PHIP is not itself a substrate of the insulin receptor. In contrast to full-length PHIP, overexpression of the PH-binding region of PHIP has a pronounced inhibitory effect on insulin-induced IRS-1 tyrosine phosphorylation levels. Furthermore, expression of this dominant-negative PHIP mutant leads to a marked attenuation of insulin-stimulated mitogen-activated protein kinase activity. We conclude that PHIP represents a novel protein ligand of the IRS-1 PH domain that may serve to link IRS-1 to the insulin receptor.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".