Active transporters as enzymes: an energetic framework applied to major facilitator superfamily and ABC importer systems
Bibliographic record
Abstract
Active membrane transporters are dynamic molecular machines that catalyse transport across a membrane by coupling solute movement to a source of energy such as ATP or a secondary ion gradient. A central question for many active transporters concerns the mechanism by which transport is coupled to a source of energy. The transport process and associated energetic coupling involve conformational changes in the transporter. For efficient transport, the conformational changes must be tightly regulated and they must link energy use to movement of the substrate across the membrane. The present review discusses active transport using the well-established energetic framework for enzyme-mediated catalysis. In particular, membrane transport systems can be viewed as ensembles consisting of low-energy and high-energy conformations. The transport process involves binding interactions that selectively stabilize the higher energy conformations, and in this way promote conformational changes in the system that are coupled to decreases in free energy and substrate translocation. The major facilitator superfamily of secondary active transporters is used to illustrate these ideas, which are then be expanded to primary active transport mediated by ABC (ATP-binding cassette) import systems, with a focus on the well-studied maltose transporter.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".