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Record W4205678608 · doi:10.3410/f.718185626.793488313

Faculty Opinions recommendation of Structural basis for Ca2+ selectivity of a voltage-gated calcium channel.

2013· dataset· en· W4205678608 on OpenAlexaff
Gerald W. Zamponi

Bibliographic record

VenueFaculty Opinions – Post-Publication Peer Review of the Biomedical Literature · 2013
Typedataset
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSelectivityChemistryCalciumConductanceBiophysicsIon channelBinding siteBiochemistryReceptorBiology

Abstract

fetched live from OpenAlex

Voltage-gated calcium (CaV) channels catalyse rapid, highly selective influx of Ca(2+) into cells despite a 70-fold higher extracellular concentration of Na(+). How CaV channels solve this fundamental biophysical problem remains unclear. Here we report physiological and crystallographic analyses of a calcium selectivity filter constructed in the homotetrameric bacterial NaV channel NaVAb. Our results reveal interactions of hydrated Ca(2+) with two high-affinity Ca(2+)-binding sites followed by a third lower-affinity site that would coordinate Ca(2+) as it moves inward. At the selectivity filter entry, Site 1 is formed by four carboxyl side chains, which have a critical role in determining Ca(2+) selectivity. Four carboxyls plus four backbone carbonyls form Site 2, which is targeted by the blocking cations Cd(2+) and Mn(2+), with single occupancy. The lower-affinity Site 3 is formed by four backbone carbonyls alone, which mediate exit into the central cavity. This pore architecture suggests a conduction pathway involving transitions between two main states with one or two hydrated Ca(2+) ions bound in the selectivity filter and supports a 'knock-off' mechanism of ion permeation through a stepwise-binding process. The multi-ion selectivity filter of our CaVAb model establishes a structural framework for understanding the mechanisms of ion selectivity and conductance by vertebrate CaV channels. PMID: 24270805 Funding information This work was supported by: NIGMS NIH HHS, United States Grant ID: T32 GM008268 NHLBI NIH HHS, United States Grant ID: R01 HL112808 Howard Hughes Medical Institute, United States NHLBI NIH HHS, United States Grant ID: R01 HL117896 NINDS NIH HHS, United States Grant ID: R01NS015751 NIGMS NIH HHS, United States Grant ID: T32GM008268 NHLBI NIH HHS, United States Grant ID: R01HL112808 NINDS NIH HHS, United States Grant ID: R01 NS015751 More Less keyboard_arrow_down

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.391
Threshold uncertainty score0.868

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.3910.161

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 head0.065
GPT teacher head0.363
Teacher spread0.299 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2013
Admission routes1
Has abstractyes

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