Drug Binding in Human P-glycoprotein Causes Conformational Changes in Both Nucleotide-binding Domains
Bibliographic record
Abstract
The human multidrug resistance P-glycoprotein (P-gp, ABCB1) uses ATP to transport many structurally diverse compounds out of the cell. It is an ABC transporter with two nucleotide-binding domains (NBDs) and two transmembrane domains (TMDs). Recently, we showed that the "LSGGQ" motif in one NBD ((531)LSGGQ(535) in NBD1; (1176)LSGGQ(1180) in NBD2) is adjacent to the "Walker A" sequence ((1070)GSSGCGKS(1077) in NBD2; (427)GNSGCGKS(434) in NBD1) in the other NBD (Loo, T. W., Bartlett, M. C., and Clarke, D. M. (2002) J. Biol. Chem. 277, 41303-41306). Drug substrates can stimulate or inhibit the ATPase activity of P-gp. Here, we report the effect of drug binding on cross-linking between the LSGGQ signature and Walker A sites (Cys(431)(NBD1)/C1176C(NBD2) and Cys(1074)(NBD2)/L531C(NBD1), respectively). Seven drug substrates (calcein-AM, demecolcine, cis(Z)-flupentixol, verapamil, cyclosporin A, Hoechst 33342, and trans(E)-flupentixol) were tested for their effect on oxidative cross-linking. Substrates that stimulated the ATPase activity of P-gp (calcein-AM, demecolcine, cis(Z)-flupentixol, and verapamil) increased the rate of cross-linking between Cys(431)(NBD1-Walker A)/C1176C(NBD2-LSGGQ) and between Cys(1074)(NBD2-Walker A)/L531C(NBD1-LSGGQ) when compared with cross-linking in the absence of drug substrate. By contrast, substrates that inhibited ATPase activity (cyclosporin A, Hoechst 33342, and trans(E)-flupentixol) decreased the rate of cross-linking. These results indicate that interaction between the LSGGQ motifs and Walker A sites must be essential for coupling drug binding to ATP hydrolysis. Drug binding in the transmembrane domains can induce long range conformational changes in the NBDs, such that compounds that stimulate or inhibit ATPase activity must decrease and increase, respectively, the distance between the Walker A and LSGGQ sequences.
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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".