New insights into the structure and function of CNNM proteins
Bibliographic record
Abstract
Magnesium (Mg2+) 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 Mg2+ 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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".