Molecular mechanism of substrate specificity in the bacterial neutral amino acid transporter LeuT
Bibliographic record
Abstract
The recently published X-ray structure of LeuT, a Na(+)/Cl(-)-dependent neurotransmitter transporter, has provided fresh impetus to efforts directed at understanding the molecular principles governing specific neurotransmitter transport. The combination of the LeuT crystal structure with the results of molecular simulations enables the functional data on specific binding and transport to be related to molecular structure. All-atom FEP and molecular dynamics (MD) simulations of LeuT embedded in an explicit membrane were performed alongside a decomposition analysis to dissect the molecular determinants of the substrate specificity of LeuT. It was found that the ligand must be in a zwitterionic (ZW) form to bind tightly to the transporter. The theoretical results on the absolute binding-free energies for leucine, alanine, and glycine show that alanine can be a potent substrate for LeuT, although leucine is preferred, which is consistent with the recent experimental data (Singh et al., Nature 2007;448:952-956). Furthermore, LeuT displays robust specificity for leucine over glycine. Interestingly, the ability of LeuT to discriminate between substrates relies on the dynamics of residues that form its binding pocket (e.g., F253 and Q250) and the charged side chains (R30-D404) from a second coordination shell. The water-mediated R30-D404 salt bridge is thought to be part of the extracellular (EC) gate of LeuT. The introduction of a polar ligand such as glycine to the water-depleted binding pocket of LeuT gives rise to structural rearrangements of the R30-D404-Q250 hydrogen-bonding network and leads to increased hydration of the binding pocket. Conformational changes associated with the broken hydrogen bond between Q250 and R30 are shown to be important for tight and selective ligand binding to LeuT.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".