Molecular basis for differential activation of p101 and p84 complexes of PI3Kγ by Ras and GPCRs
Bibliographic record
Abstract
Abstract Class IB phosphoinositide 3-kinase (PI3Kγ) is activated in immune cells by diverse stimuli and can form two distinct complexes, with the p110γ catalytic subunit binding to either p101 or p84 regulatory subunits. These two complexes are differentially activated by G-protein coupled receptors (GPCRs) and Ras, but the molecular details of this activation are still unclear. Using a combination of X-ray crystallography, HDX-MS, EM, molecular modeling, and biochemical assays we reveal molecular differences between the two p110γ-p84 and p110γ-p101 complexes that explain their differential activation. The structure of p110γ-p84 shows a similar assembly to p110γ-p101 at the p110γ interface, however the interface in p110γ-p84 is dynamic and is evolutionarily conserved to be less stable compared to p110γ-p101. The p110γ-p84 complex is only weakly recruited to membranes by Gβγ subunits alone and requires recruitment by Ras to allow for Gβγ activation through an interaction with the p110γ helical domain. The interfaces of the p101 GBD with Gβγ, and the p110γ helical domain with Gβγ were determined using computational alphafold2 modeling and HDX-MS. There are distinct differences in the C-terminal domain of p84 and p101, which allows p101 to bind Gβγ subunits, while p84 does not. The two Gβγ interfaces in p110γ and p101 are distinct, revealing how unique mutants of Gβγ cause differential disruption of PI3Kγ complex activation. Overall, our work provides key insight into the molecular basis for how different PI3Kγ complexes are activated.
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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 | 0.000 |
| 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".