Structure-Function analysis of the CTLA-4 interaction with PP2A
Bibliographic record
Abstract
BACKGROUND: CTLA-4 functions primarily as an inhibitor of T cell activation. There are several candidate explanations as to how CTLA-4 modulates T cell responses, but the exact mechanism remains undefined. The tail of CTLA-4 does not have any intrinsic enzymatic activity but is able to associate with several signaling molecules including the serine/threonine phosphatase PP2A. PP2A is a heterotrimeric molecule comprised of a regulatory B subunit associated with a core dimer of a scaffolding (A) and a catalytic (C) subunit. RESULTS: Here, we performed an analysis of the human CTLA-4 interface interacting with PP2A. We show that PP2A interacts with the cytoplasmic tail of CTLA-4 in two different sites, one on the lysine rich motif, and the other on the tyrosine residue located at position 182 (but not the tyrosine 165 of the YVKM motif). Although the interaction between CTLA-4 and PP2A was not required for inhibition of T cell responses, it was important for T cell activation by inverse agonists of CTLA-4. Such an interaction was functionally relevant because the inverse agonists induced IL-2 production in an okadaic acid-dependent manner. CONCLUSION: Our studies demonstrate that PP2A interacts with the cytoplasmic tail of human CTLA-4 through two motifs, the lysine rich motif centered at lysine 155 and the tyrosine residue 182. This interaction and the phosphatase activity of PP2A are important for CTLA-4-mediated T cell activation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".