Substrate-induced Conformational Changes in the Transmembrane Segments of Human P-glycoprotein
Bibliographic record
Abstract
The human multidrug resistance P-glycoprotein (P-gp, ABCB1) is quite promiscuous in that it can transport a broad range of structurally diverse compounds out of the cell. We hypothesized that the transmembrane (TM) segments that constitute the drug-binding site are quite mobile such that drug binding occurs through a "substrate-induced fit" mechanism. Here, we used cysteine-scanning mutagenesis and oxidative cross-linking to test for substrate-induced changes in the TM segments. Pairs of cysteines were introduced into a Cys-less P-gp and the mutants treated with oxidant (copper phenanthroline) in the presence or absence of various drug substrates. We show that cyclosporin A promoted cross-linking between residues P350C(TM6)/G939C(TM11), while colchicine and demecolcine promoted cross-linking between residues P350C(TM6)/V991C(TM12). Progesterone promoted cross-linking between residues P350C(TM6)/A935C(TM11), P350C(TM6)/G939C(TM11), as well as between residues P350C(TM6)/V991C(TM12). Other substrates such as vinblastine, verapamil, cis-(Z)-flupenthixol or trans-(E)-flupenthixol did not induce cross-linking at these sites. These results provide direct evidence that the packing of the TM segments in the drug-binding site is changed when P-gp binds to a particular substrate. The induced-fit mechanism explains how P-gp can accommodate a broad range of compounds.
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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".