Protein-protein interaction–interfering peptide rescues dysregulated NMDA receptor signaling
Bibliographic record
Abstract
The complex and heterogeneous genetic architecture of neuropsychiatric illnesses compels us to look beyond individual risk genes for therapeutic strategies and target the interactive dynamics and convergence of their protein products. A mechanistic substrate for convergence of synaptic neuropsychiatric risk genes are protein-protein interactions (PPIs) in the N-methyl-D-aspartate receptor (NMDAR) complex. NMDAR hypofunction in schizophrenia is associated with hypoactivity of Src kinase, resulting from convergent alterations in PPIs of Src with its partners. Of these, the association of Src with PSD-95, which inhibits the activity of this kinase in the NMDAR complex, is known to be increased in schizophrenia. Here, we devised a strategy to suppress the inhibition of Src by PSD-95 by employing a cell-penetrating and Src-activating PSD-95 inhibitory peptide (TAT-SAPIP). TAT-SAPIP enhanced synaptic NMDAR currents in Src+/- and Sdy-/- mice manifesting NMDAR hypofunction phenotypes. Chronic intracerebroventricularly (ICV) injection of TAT-SAPIP rescued cognitive deficits in trace fear conditioning in Src +/- mice. Moreover, TAT-SAPIP enhanced Src activity in synaptoneurosomes derived from dorsolateral prefrontal cortex of 14 patients. We propose blockade of the Src-PSD-95 interaction as a proof of concept for the use of interfering peptides as a therapeutic strategy to reverse NMDAR hypofunction in schizophrenia and other illnesses.
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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 | 0.000 |
| 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 | 0.001 |
| 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".