Bibliographic record
Abstract
Ion channels are integral membrane proteins that undergo important conformational changes as they open and close to control transmembrane flux of different ions. The molecular underpinnings of these dynamic conformational rearrangements are difficult to ascertain using current structural methods. Several functional approaches have been used to understand two- and three-dimensional dynamic structures of ion channels, based on the reactivity of the cysteine side-chain. Two-dimensional structural rearrangements, such as changes in the accessibility of different parts of the channel protein to the bulk solution on either side of the membrane, are used to define movements within the permeation pathway, such as those that open and close ion channel gates. Three-dimensional rearrangements – in which two different parts of the channel protein change their proximity during conformational changes – are probed by cross-linking or bridging together two cysteine side-chains. Particularly useful in this regard are so-called metal bridges formed when two or more cysteine side-chains form a high-affinity binding site for metal ions such as Cd2+ or Zn2+. This review describes the use of these different techniques for the study of ion channel dynamic structure and function, including a comprehensive review of the different kinds of conformational rearrangements that have been studied in different channel types via the identification of intra-molecular metal bridges. Factors that influence the affinities and conformational sensitivities of these metal bridges, as well as the kinds of structural inferences that can be drawn from these studies, are also discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".