PTP-MEG2 regulates quantal size and fusion pore opening through two distinct structural bases and substrates
Bibliographic record
Abstract
ABSTRACT Tyrosine phosphorylation of secretion machinery proteins is a crucial regulatory mechanism for exocytosis. However, the participation of protein tyrosine phosphatases (PTPs) in different exocytosis stages has not been defined. Here we demonstrated that PTP-MEG2 controls multiple steps of catecholamine secretion. Biochemical and crystallographic analyses revealed key residues that the interactions between govern the PTP-MEG2 and NSF-pY 83 site, specify PTP-MEG2 substrate selectivity and modulate the fusion of catecholamine-containing vesicles. Unexpectedly, delineation of PTP-MEG2 mutants along with the NSF binding interface revealed that PTP-MEG2 controls the fusion pore opening through non-NSF dependent mechanisms. Utilizing bioinformatics search and biochemical and electrochemical screening approaches, we discovered that PTP-MEG2 regulates the opening and extension of the fusion pore by dephosphorylating the DYNAMIN2-pY 125 and MUNC18-1-pY 145 site. Further structural and biochemical analysis confirmed the interaction of PTP-MEG2 with MUNC18-1-pY 145 or DYNAMIN2-pY 125 through a distinct structural basis compared with that of the NSF-pY 83 site. Our studies extended mechanistic insights in complex exocytosis processes. HIGHLIGHTS PTP-MEG2 regulates multiple steps of exocytosis. A crystal structure of the PTP-MEG2/phosphor-NSF-pY 83 segment was obtained. Functional delineation of the PTP-MEG2/NSF interface led to the discovery of new PTP-MEG2 substrates. PTP-MEG2 regulates fusion pore opening and extension through the DYNAMIN2-pY 125 site and MUNC18-1 pY 145 site. The distinct structural basis of the recognition of substrates by PTP-MEG2 allows selective inhibitor design.
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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".