Structural prediction of GluN3 NMDA receptors
Bibliographic record
Abstract
-aspartate (NMDA) receptors are heterotetrametric ion channels composed of two obligatory GluN1 subunits and two alternative GluN2 or GluN3 subunits, forming GluN1-N2, GluN1-N3, and GluN1-N2-N3 type of NMDA receptors. Extensive research has focused on the functional and structural properties of conventional GluN1-GluN2 NMDA receptors due to their early discovery and high expression levels. However, the knowledge of unconventional GluN1-N3 NMDA receptors remains limited. In this study, we modeled the GluN1-N3A, GluN1-N3B, and GluN1-N3A-N3B NMDA receptors using deep-learned protein-language predication algorithms AlphaFold and RoseTTAFold All-Atom. We then compared these structures with GluN1-N2 and GluN1-N3A receptor cryo-EM structures and found that GluN1-N3 receptors have distinct properties in subunit arrangement, domain swap, and domain interaction. Furthermore, we predicted the agonist- or antagonist-bound structures, highlighting the key molecular-residue interactions. Our findings shed new light on the structural and functional diversity of NMDA receptors and provide a new direction for drug development. This study uses advanced AI algorithms to model GluN1-N3 NMDA receptors, revealing unique structural properties and interactions compared to conventional GluN1-N2 receptors. By highlighting key molecular-residue interactions and predicting ligand-bound structures, our research enhances the understanding of NMDA receptor diversity and offers new insights for targeted drug development.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".