Stoichiometry and Affinity of Nucleotide Binding to P-Glycoprotein during the Catalytic Cycle
Bibliographic record
Abstract
Drug transport mediated by P-glycoprotein (Pgp) is driven by hydrolysis of ATP at the two cytosolic nucleotide binding domains. However, little is currently known concerning the stoichiometry of nucleotide binding and how both stoichiometry and binding affinity change during the catalytic cycle of the transporter. To address this issue, we used fluorescence techniques to measure both the number of nucleotides bound to P-glycoprotein during various stages of the catalytic cycle and the affinity of nucleotide binding. Results showed that resting state P-glycoprotein bound two molecules of the fluorescent nucleotide derivative, 2'(3')-O-(2,4,6-trinitrophenyl)adenosine 5'-triphosphate (TNP-ATP), whereas the vanadate-trapped transition state bound only one nucleotide molecule. Both resting and transition state P-glycoprotein showed similar affinity for TNP-ATP/TNP-ADP and unlabeled ATP/ADP. Following binding of various drugs, resting state P-glycoprotein displayed a higher affinity for nucleotides, up to 4-fold depending on the compound used. In contrast, the transition state showed substantially lower (up to 3-fold) nucleotide binding affinity when the drug binding site(s) is/are occupied. These results indicate that both nucleotide binding domains of P-glycoprotein are likely to be occupied with either ATP (or ADP) in the resting state and the transition state in the absence of transport substrates. Drugs alter the binding affinity to favor association of ATP with P-glycoprotein at the start of the catalytic cycle and release of ADP from the transition state following nucleotide hydrolysis.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".