Regulation of glutamate receptors by striatal‐enriched tyrosine phosphatase 61 (STEP<sub>61</sub>)
Bibliographic record
Abstract
Abstract Phosphorylation regulates glutamate receptor trafficking. The cytosolic C‐terminal domains of both NMDA receptors (NMDARs) and AMPA receptors (AMPARs) have distinct motifs, which are substrates for serine/threonine and tyrosine phosphorylation. Decades of research have shown how phosphorylation of glutamate receptors mediates protein binding and receptor trafficking, ultimately controlling synaptic transmission and plasticity. STEP is a protein tyrosine phosphatase (also known as PTPN5), with several isoforms resulting from alternative splicing. Targets of STEP include a variety of important synaptic substrates, among which are the tyrosine kinase Fyn and glutamate receptors. In particular, STEP61, the longest isoform, dephosphorylates the NMDAR subunit GluN2B and strongly regulates the expression of NMDARs at synapses. This interplay between STEP, Fyn and GluN2B‐containing NMDARs has been characterized by multiple groups. More recently, STEP61 was shown to bind to AMPARs in a subunit‐specific manner and differentially regulate synaptic NMDARs and AMPARs. Because of its many effects on synaptic proteins, STEP has been implicated in regulating excitatory synapses during plasticity and playing a role in synaptic dysfunction in a variety of neurological disorders. In this review, we will highlight the ways in which STEP61 differentially regulates NMDARs and AMPARs, as well as its role in plasticity and disease. image
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".