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Record W2170989849 · doi:10.1101/cshperspect.a009522

Dynamics Intrinsic to Cystic Fibrosis Transmembrane Conductance Regulator Function and Stability

2013· review· en· W2170989849 on OpenAlexafffund
P. A. Chong, Pradeep Kota, Nikolay V. Dokholyan, Julie D. Forman‐Kay

Bibliographic record

VenueCold Spring Harbor Perspectives in Medicine · 2013
Typereview
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsUniversity of TorontoHospital for Sick Children
FundersNational Institute of General Medical SciencesCystic Fibrosis CanadaCystic Fibrosis Foundation TherapeuticsNational Institutes of HealthCystic Fibrosis Foundation
KeywordsCystic fibrosis transmembrane conductance regulatorGatingBiophysicsFolding (DSP implementation)Cyclic nucleotide-binding domainCystic fibrosisChemistryTransmembrane domainMutationFunction (biology)Cell biologyNucleotideBiologyBiochemistryGeneticsMembraneGene

Abstract

fetched live from OpenAlex

The cystic fibrosis transmembrane conductance regulator (CFTR) requires dynamic fluctuations between states in its gating cycle for proper channel function, including changes in the interactions between the nucleotide-binding domains (NBDs) and between the intracellular domain (ICD) coupling helices and NBDs. Such motions are also linked with fluctuating phosphorylation-dependent binding of CFTR's disordered regulatory (R) region to the NBDs and partners. Folding of CFTR is highly inefficient, with the marginally stable NBD1 sampling excited states or folding intermediates that are aggregation-prone. The severe CF-causing F508del mutation exacerbates the folding inefficiency of CFTR and leads to impaired channel regulation and function, partly as a result of perturbed NBD1-ICD interactions and enhanced sampling of these NBD1 excited states. Increased knowledge of the dynamics within CFTR will expand our understanding of the regulated channel gating of the protein as well as of the F508del defects in folding and function.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.768
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.038
GPT teacher head0.336
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

Citations28
Published2013
Admission routes2
Has abstractyes

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