Defining the Drug-binding Site in the Human Multidrug Resistance P-glycoprotein Using a Methanethiosulfonate Analog of Verapamil, MTS-verapamil
Bibliographic record
Abstract
Defining the residues involved in the binding of a substrate provides insight into how the human multidrug resistance P-glycoprotein (P-gp) can transport a wide range of structurally diverse compounds out of the cell. Because verapamil is the most potent stimulator of P-gp ATPase activity, we synthesized a thiol-reactive analog of verapamil (MTS-verapamil) and used it with cysteine-scanning mutagenesis to identify the reactive residues within the drug-binding domain of P-gp. MTS-verapamil stimulated the ATPase activity of Cys-less P-gp and had a K(m) value (25 microM) that was similar to that of verapamil. 252 P-gp mutants containing a single cysteine within the predicted transmembrane (TM) segments were expressed in HEK 293 cells and purified by nickel-chelate chromatography and assayed for inhibition by MTS-verapamil. The activities of 15 mutants, Y118C (TM2), V125C (TM2), S222C (TM4), L339C (TM6), A342C (TM6), A729C (TM7), A841C (TM9), N842C (TM9), I868C (TM10), A871C (TM10), F942C (TM11), T945C (TM11), V982C (TM12), G984C (TM12), and A985C (TM12), were inhibited by MTS-verapamil. Four mutants, S222C (TM4), L339C (TM6), A342C (TM6), and G984C (TM12), were significantly protected from inhibition by MTS-verapamil by pretreatment with verapamil. Less protection was observed in mutants I868C (TM10), F942C (TM11) and T945C (TM11). These results indicate that residues in TMs 4, 6, 10, 11, and 12 must contribute to the binding of verapamil.
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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".