Structural basis of excitatory amino acid transporter 3 substrate recognition
Bibliographic record
Abstract
Excitatory amino acid transporters (EAATs) reside on cell surfaces and uptake substrates, including L-glutamate, L-aspartate, and D-aspartate, using ion gradients. Among five EAATs, EAAT3 is the only isoform that can efficiently transport L-cysteine, a substrate for glutathione synthesis. Recent studies suggest that EAAT3 also transports the oncometabolite R-2-hydroxyglutarate (R-2HG). Here, we examined the structural basis of substrate recognition by determining the cryogenic electron microscopy (cryo-EM) structures of EAAT3 bound to different substrates. We found that L-cysteine binds to EAAT3 in thiolate form, and EAAT3 recognizes different substrates by fine-tuning local conformations of the coordinating residues. However, using purified human EAAT3, we could not observe R-2HG binding or transport. Imaging of EAAT3 bound to L-cysteine revealed several conformational states, including an outward-facing state with a semi-open gate and a disrupted sodium-binding site. These structures demonstrate that the full gate closure, coupled with the binding of the last sodium ion, occurs after substrate binding. Furthermore, we observed that different substrates affect how the transporter distributes between a fully outward-facing conformation and intermediate occluded states on a path to the inward-facing conformation, suggesting that translocation rates are substrate-dependent.
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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 | 0.001 |
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".