Identification of Residues in the Drug Translocation Pathway of the Human Multidrug Resistance P-glycoprotein by Arginine Mutagenesis
Bibliographic record
Abstract
P-glycoprotein (P-gp, ATP-binding cassette B1) is a drug pump that extracts toxic drug substrates from the plasma membrane and catalyzes their ATP-dependent efflux. To map the residues in the drug translocation pathway, we performed arginine-scanning mutagenesis on all transmembrane (TM) segments (total = 237 residues) of a P-gp processing mutant (G251V) defective in folding (15% maturation efficiency) (glycosylation state used to monitor folding). The rationale was that arginines introduced into the drug-binding sites would mimic drug rescue and enhance maturation of wild-type or processing mutants of P-gp. It was found that 38 of the 89 mutants that matured had enhanced maturation. Enhancer mutations were found in 11 of the 12 TM segments with the largest number found in TMs 6 and 12 (seven in each), TMs that are critical for P-gp-drug substrate interactions. Modeling of the TM segments showed that the enhancer arginines were found on the hydrophilic face, whereas inhibitory arginines were located on a hydrophobic face that may be in contact with the lipid bilayer. It was found that many of the enhancer arginines caused large alterations in P-gp-drug interactions in ATPase assays. For example, mutants A302R (TM5), L339R (TM6), G872R (TM10), F942R (TM11), Q946R (TM11), V982R (TM12), and S993R (TM12) reduced the apparent affinity for verapamil by approximately 10-fold, whereas the F336R (TM6) and M986R (TM12) mutations caused at least a 10-fold increase in apparent affinity for rhodamine B. The results suggest that P-gp contains a large aqueous-filled drug translocation pathway with multiple drug-binding sites that can accommodate the bulky arginine side chains to promote folding of the protein.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".