Molecular insight on the role of the phosphoinositide PIP3 in regulating the protein kinases Akt, PDK1, and BTK
Bibliographic record
Abstract
Protein kinases are master regulators of myriad processes in eukaryotic cells playing critical roles in growth, metabolism, cellular differentiation, and motility. A subclass of protein kinases is regulated by their ability to be localized and activated by the phosphoinositide phosphatidylinositol (3,4,5)-trisphosphate (PIP3). This includes multiple members of the AGC and TEC family kinases, which contain PIP3 binding pleckstrin homology (PH) domains. It has been postulated that they can be activated by PIP3-mediated disruption of autoinhibitory interactions between their kinase domains and PH domains. There has been considerable controversy based on differing molecular models for how these kinases are regulated by lipid binding and post-translational modifications. This review focuses on understanding the molecular underpinnings for how the PH domains of these enzymes regulate kinase activity and what role PIP3 plays in pathway activation. A specific focus is on the integration of experimental data derived from X-ray crystallography, cryo-electron microscopy, and hydrogen deuterium exchange mass spectrometry along with recent advances in artifical intelligence enabled protein modeling. The main lipid-binding enzymes described are the AGC protein kinases 3-phosphoinositide-dependent kinase (PDK1) and Akt, and the TEC family kinase, Bruton's agammaglobulinemia tyrosine kinase (BTK).
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".