Mapping the Binding Site of the Inhibitor Tariquidar That Stabilizes the First Transmembrane Domain of P-glycoprotein
Bibliographic record
Abstract
ABC (ATP-binding cassette) transporters are clinically important because drug pumps like P-glycoprotein (P-gp, ABCB1) confer multidrug resistance and mutant ABC proteins are responsible for many protein-folding diseases such as cystic fibrosis. Identification of the tariquidar-binding site has been the subject of intensive molecular modeling studies because it is the most potent inhibitor and corrector of P-gp. Tariquidar is a unique P-gp inhibitor because it locks the pump in a conformation that blocks drug efflux but activates ATPase activity. In silico docking studies have identified several potential tariquidar-binding sites. Here, we show through cross-linking studies that tariquidar most likely binds to sites within the transmembrane (TM) segments located in one wing or at the interface between the two wings (12 TM segments form 2 divergent wings). We then introduced arginine residues at all positions in the 12 TM segments (223 mutants) of P-gp. The rationale was that a charged residue in the drug-binding pocket would disrupt hydrophobic interaction with tariquidar and inhibit its ability to rescue processing mutants or stimulate ATPase activity. Arginines introduced at 30 positions significantly inhibited tariquidar rescue of a processing mutant and activation of ATPase activity. The results suggest that tariquidar binds to a site within the drug-binding pocket at the interface between the TM segments of both structural wings. Tariquidar differed from other drug substrates, however, as it stabilized the first TM domain. Stabilization of the first TM domain appears to be a key mechanism for high efficiency rescue of ABC processing mutants that cause disease.
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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 | 0.000 |
| 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".