Characterising ion channel structure and dynamics using fluorescence spectroscopy techniques
Bibliographic record
Abstract
Ion channels undergo major conformational changes that lead to channel opening and ion conductance. Deciphering these structure-function relationships is paramount to understanding channel physiology and pathophysiology. Cryo-electron microscopy, crystallography and computer modelling provide atomic-scale snapshots of channel conformations in non-cellular environments but lack dynamic information that can be linked to functional results. Biophysical techniques such as electrophysiology, on the other hand, provide functional data with no structural information of the processes involved. Fluorescence spectroscopy techniques help bridge this gap in simultaneously obtaining structure-function correlates. These include voltage-clamp fluorometry, Förster resonance energy transfer, ligand binding assays, single molecule fluorescence and their variations. These techniques can be employed to unearth several features of ion channel behaviour. For instance, they provide real time information on local and global rearrangements that are inherent to channel properties. They also lend insights in trafficking, expression, and assembly of ion channels on the membrane surface. These methods have the advantage that they can be carried out in either native or heterologous systems. In this review, we briefly explain the principles of fluorescence and how these have been translated to study ion channel function. We also report several recent advances in fluorescence spectroscopy that has helped address and improve our understanding of the biophysical behaviours of different ion channel families.
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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 | 0.003 | 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) | 0.002 | 0.002 |
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".