Artificial intelligence insight on structural basis and small molecule binding niches of NMDA receptor
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
NMDA receptors are critical to neuronal activity and play essential roles in synaptic transmission, learning, and memory. Despite significant advances in X-ray crystallography and cryo-electron microscopy (cryo-EM), the structural diversity of NMDA receptors across species and the variations among receptor subtypes within the same species remain insufficiently explored. Additionally, several key small molecule binding sites, such as those for agonists, antagonists, and allosteric modulators, have not been fully characterized. In this study, we utilized state-of-the-art artificial intelligence algorithms to model NMDA receptors across multiple species and found that they all adopted a bouquet-like dimer-of-dimer structure. By comparing these models with cryo-EM resolved structures, we assessed the accuracy of the predictions and complemented the structural data with detailed models of transmembrane domain regions, which are traditionally challenging for experimental methods. Furthermore, through the integration of AI-based prediction tools and molecular dynamic simulations, we highlighted potential binding sites for agonists, competitive antagonists, and pore blockers at amino acid resolution. This AI-enhanced approach builds traditional structural biology techniques, revealing that NMDA receptors from different species adopt highly similar three-dimensional architectures, while also exhibiting subtype-specific structural features. Furthermore, our identification of ligand binding pockets at the amino acid resolution provides a more detailed understanding of receptor-ligand interactions, offering potential templates for rational drug design and optimization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it