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Record W4377940222 · doi:10.1111/febs.16872

New insights into the structure and function of CNNM proteins

2023· review· en· W4377940222 on OpenAlexafffund
Yu Seby Chen, Kalle Gehring

Bibliographic record

VenueFEBS Journal · 2023
Typereview
Languageen
FieldNursing
TopicMagnesium in Health and Disease
Canadian institutionsMcGill UniversityUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDivalentTransmembrane domainTransmembrane proteinBiologyBiochemistryFunction (biology)Cyclic nucleotide-binding domainTransporterProtein domainChemistryCell biologyComputational biologyBiophysicsNucleotideAmino acidGene

Abstract

fetched live from OpenAlex

Magnesium (Mg 2+ ) is the most abundant divalent cation in cells and plays key roles in almost all biological processes. CBS‐pair domain divalent metal cation transport mediators (CNNMs) are a newly characterized class of Mg 2+ transporters present throughout biology. Originally discovered in bacteria, there are four CNNM proteins in humans, which are involved in divalent cation transport, genetic diseases, and cancer. Eukaryotic CNNMs are composed of four domains: an extracellular domain, a transmembrane domain, a cystathionine‐β‐synthase (CBS)‐pair domain, and a cyclic nucleotide‐binding homology domain. The transmembrane and CBS‐pair core are the defining features of CNNM proteins with over 20 000 protein sequences known from over 8000 species. Here, we review the structural and functional studies of eukaryotic and prokaryotic CNNMs that underlie our understanding of their regulation and mechanism of ion transport. Recent structures of prokaryotic CNNMs confirm the transmembrane domain mediates ion transport with the CBS‐pair domain likely playing a regulatory role through binding divalent cations. Studies of mammalian CNNMs have identified new binding partners. These advances are driving progress in understanding this deeply conserved and widespread family of ion transporters.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score0.521

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.046
GPT teacher head0.337
Teacher spread0.291 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations11
Published2023
Admission routes2
Has abstractyes

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