Simultaneous Binding of Two Different Drugs in the Binding Pocket of the Human Multidrug Resistance P-glycoprotein
Bibliographic record
Abstract
The human multidrug resistance P-glycoprotein (P-gp, ABCB1) transports a wide variety of structurally diverse compounds out of the cell. The drug-binding pocket of P-gp is located in the transmembrane domains. Although occupation of the drug-binding pocket by one molecule is sufficient to activate the ATPase activity of P-gp, the drug-binding pocket may be large enough to accommodate two different substrates at the same time. In this study, we used cysteine-scanning mutagenesis to test whether P-gp could simultaneously interact with the thiol-reactive drug substrate, Tris-(2-maleimidoethyl)amine (TMEA) and a second drug substrate. TMEA is a cross-linker substrate of P-gp that allowed us to test for stimulation of cross-linking by a second substrate such as calcein-acetoxymethyl ester, colchicine, demecolcine, cyclosporin A, rhodamine B, progesterone, and verapamil. We report that verapamil induced TMEA cross-linking of mutant F343C(TM6)/V982C(TM12). By contrast, no cross-linked product was detected in mutants F343C(TM6), V982C(TM12), or F343C(TM6)/V982C(TM12) in the presence of TMEA alone. The verapamil-stimulated ATPase activity of mutant F343C(TM6)/V982C(TM12) in the presence of TMEA decreased with increased cross-linking of the mutant protein. These results show that binding of verapamil must induce changes in the drug-binding pocket (induced-fit mechanism) resulting in exposure of residues F343C(TM6)/V982C(TM12) to TMEA. The results also indicate that the common drug-binding pocket in P-gp is large enough to accommodate both verapamil and TMEA simultaneously and suggests that the substrates must occupy different regions in the common drug-binding pocket.
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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 | 0.001 | 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".