Determinants of the Substrate Specificity of Multidrug Resistance Protein 1
Bibliographic record
Abstract
Human multidrug resistance protein 1 (MRP1) confers resistance to many natural product chemotherapeutic agents and actively transports structurally diverse organic anion conjugates. We previously demonstrated that two hydrogen-bonding amino acid residues in the predicted transmembrane 17 (TM17) of MRP1, Thr(1242) and Trp(1246), were important for drug resistance and 17beta-estradiol 17-(beta-d-glucuronide) (E(2)17betaG) transport. To determine whether other residues with hydrogen bonding potential within TM17 influence substrate specificity, we replaced Ser(1233), Ser(1235), Ser(1237), Gln(1239), Thr(1241), and Asn(1245) with Ala and Tyr(1236) and Tyr(1243) with Phe. Mutations S1233A, S1235A, S1237A, and Q1239A had no effect on any substrate tested. In contrast, mutations Y1236F and T1241A decreased resistance to vincristine but not to VP-16, doxorubicin, and epirubicin. Mutation Y1243F reduced resistance to all drugs tested by 2-3-fold. Replacement of Asn(1245) with Ala also decreased resistance to VP-16, doxorubicin, and epirubicin but increased resistance to vincristine. This mutation also decreased E(2)17betaG transport approximately 5-fold. Only mutation Y1243F altered the ability of MRP1 to transport both leukotriene 4 and E(2)17betaG. Together with our previous results, these findings suggest that residues with side chain hydrogen bonding potential, clustered in the cytoplasmic half of TM17, participate in the formation of a substrate binding site.
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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) | 0.002 | 0.001 |
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".