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Record W2162478729 · doi:10.1042/bj20140675

Active transporters as enzymes: an energetic framework applied to major facilitator superfamily and ABC importer systems

2015· review· en· W2162478729 on OpenAlexafffund
Brian H. Shilton

Bibliographic record

VenueBiochemical Journal · 2015
Typereview
Languageen
FieldMedicine
TopicDrug Transport and Resistance Mechanisms
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMajor facilitator superfamilyTransporterActive transportBiophysicsChemistryMembrane transport proteinBiochemistryATP-binding cassette transporterTransport proteinMembrane transportSUPERFAMILYMembraneBiologyReceptor

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.960
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.335
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations27
Published2015
Admission routes2
Has abstractyes

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