Activity and dynamics of p110α are not differentially modulated by regulatory subunit isoforms
Bibliographic record
Abstract
Class IA phosophoinositide kinases (PI3Ks) are master regulators of growth, metabolism, and immunity. The class IA PI3Ks are a heterodimer composed of a p110 catalytic subunit and one of five possible regulatory subunits (p85α, p85β, p55γ, p55α, p50α). The regulatory subunit plays critical roles in stability, inhibition, and activation of the p110 catalytic subunit. The p110α catalytic subunit frequently contains activating mutations in human cancer, with many of these mutations altering the interaction between catalytic and regulatory subunits. It has been found that different regulatory subunits play unique roles in human disease, but it is unknown how these different subunits regulate p110α. Here, using a synergy of biochemical assays and hydrogen deuterium exchange mass spectrometry (HDX-MS) we examined how the five different regulatory subunits inhibit, activate, and interact with the p110α catalytic subunit. We find that there are no significant differences in lipid kinase activity or in membrane recruitment between the different heterodimer complexes. HDX-MS in the presence and absence of an activating phosphopeptide also showed only minor conformational differences between different regulatory subunit complexes. Overall, our work reveals that the different regulatory subunits interact with and inhibit p110α in a similar fashion at a molecular level.
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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 | 0.000 |
| 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".