Structural Studies of Ion Channels: Achievements, Problems, and Perspectives
Bibliographic record
Abstract
Abstract The superfamily of membrane proteins known as P-loop channels encompasses potassium, sodium, and calcium channels, as well as TRP channels and ionotropic glutamate receptors. An increasing number of crystal and cryo-EM structures are uncovering both general and specific features of these channels. Fundamental folding principles, the arrangement of structural segments, key residues that influence ionic selectivity, gating, and binding sites for toxins and medically relevant ligands have now been firmly established. The advent of AlphaFold2 models represents another significant step in computationally predicting protein structures. Comparison of experimental P-loop channel structures with their corresponding AlphaFold2 models shows consistent folding patterns in experimentally resolved regions. Despite this remarkable progress, many crucial structural details, particularly important for predicting the outcomes of mutations and designing new medically relevant ligands, remain unresolved. Certain methodological challenges currently hinder the direct assessment of such details. Until the next methodological breakthrough occurs, a promising approach to analyzing ion channel structures in greater depth involves integrating various experimental and theoretical methods.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".