Two mechanisms of ion selectivity in protein binding sites
Bibliographic record
Abstract
A theoretical framework is presented to clarify the molecular determinants of ion selectivity in protein binding sites. The relative free energy of a bound ion is expressed in terms of the main coordinating ligands coupled to an effective potential of mean force representing the influence of the rest of the protein. The latter is separated into two main contributions. The first includes all the forces keeping the ion and the coordinating ligands confined to a microscopic subvolume but does not prevent the ligands from adapting to a smaller or larger ion. The second regroups all the remaining forces that control the precise geometry of the coordinating ligands best adapted to a given ion. The theoretical framework makes it possible to delineate two important limiting cases. In the limit where the geometric forces are dominant (rigid binding site), ion selectivity is controlled by the ion-ligand interactions within the matching cavity size according to the familiar "snug-fit" mechanism of host-guest chemistry. In the limit where the geometric forces are negligible, the ion and ligands behave as a "confined microdroplet" that is free to fluctuate and adapt to ions of different sizes. In this case, ion selectivity is set by the interplay between ion-ligand and ligand-ligand interactions and is controlled by the number and the chemical type of ion-coordinating ligands. The framework is illustrated by considering the ion-selective binding sites in the KcsA channel and the LeuT transporter.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".