Functionally additive fixed positive and negative charges in the CFTR channel pore control anion binding and conductance
Bibliographic record
Abstract
Ion channels use charged amino-acid residues to attract oppositely charged permeant ions into the channel pore. In the cystic fibrosis transmembrane conductance regulator (CFTR) Cl − channel, a number of arginine and lysine residues have been shown to be important for Cl − permeation. Among these, two in close proximity in the pore—Lys 95 and Arg 134 —are indispensable for anion binding and high Cl − conductance, suggesting that high positive charge density is required for pore function. Here we used mutagenesis and functional characterization to show that a nearby pore-lining negatively charged residue (Glu 92 ) plays a functionally additive role with these two positive charges. While neutralization of this negative charge had little effect on anion binding or Cl − conductance, such neutralization was able to reverse the detrimental effects of removing the positive charge at either Lys 95 or Arg 134 , as well as the similar effects of introducing a negative charge at a neighboring residue (Ser 1141 ). Furthermore, neutralization of Glu 92 greatly increased the susceptibility of the channel to blockage by divalent S 2 O 3 2− anions, mimicking the effect of introducing additional positive charge in this region; this effect was reversed by concurrent neutralization of either Lys 95 or Arg 134 . Across a panel of mutant channels that introduced or removed fixed charges at these four positions, we found that many pore properties are dependent on the overall charge or charge density. We propose that the CFTR pore uses a combination of positively and negatively charged residues to optimize the anion binding and Cl − conductance properties of the channel.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".