Molecular mechanism of activation of class IA phosphoinositide 3-kinases (PI3Ks) by membrane-localized HRas
Bibliographic record
Abstract
), and inappropriate activation of this pathway is implicated in a multitude of human diseases, including cancer, inflammation, and primary immunodeficiencies. Class IA PI3Ks are activated downstream of the Ras superfamily of GTPases, and Ras-PI3K interaction plays a key role in promoting tumor formation and maintenance in Ras-driven tumors. Investigating the detailed molecular events in the Ras-PI3K interaction has been challenging because it occurs on a membrane surface. Here, using maleimide-functionalized lipid vesicles, we successfully generated membrane-resident HRas and evaluated its effect on PI3K signaling in lipid kinase assays and through analysis with hydrogen-deuterium exchange MS. We screened all class IA PI3K isoforms and found that HRas activates both p110α and p110δ isoforms but does not activate p110β. The p110α and p110δ activation by Ras was synergistic with activation by a soluble phosphopeptide derived from receptor tyrosine kinases. Hydrogen-deuterium exchange MS revealed that membrane-resident HRas, but not soluble HRas, enhances conformational changes associated with membrane binding by increasing membrane recruitment of both p110α and p110δ. Together, these results afford detailed molecular insight into the Ras-PI3K signaling complex, provide a framework for screening Ras inhibitors, and shed light on the isoform specificity of Ras-PI3K interactions in a native membrane context.
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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".