Mutational analysis of polar amino acid residues within predicted transmembrane helices of Multidrug Resistance Protein 1 (ABCC1): Effect on substrate specificity
Bibliographic record
Abstract
Human Multidrug Resistance Protein1 (MRP1) is an ATP Binding Cassette (ABC) transporter that confers resistance to many natural product chemotherapeutic agents and can transport structurally diverse conjugated organic anions. In addition to two cytoplasmic nucleotide binding domains (NBDs), MRP1 has three polytopic membrane‐spanning domains (MSDs) with a total of 17 transmembrane (TM) helices. Using site‐directed mutagenesis, we demonstrated that certain polar residues within a number of TM helices are determinants of MRP1 substrate specificity or overall activity. For example, mutations T550A and T556A in TM10, N597A and S605A in TM11, E1089Q and K1092 in TM14, as well as Y1236F and T1241A in TM17 only modulated the drug resistance profile of MRP1. In contrast, Y568A in TM10, S1097A and N1100A in TM14, as well as T1242A in TM17 only altered the ability of MRP1 to transport the conjugated organic anion, 17β‐estradiol 17‐(β‐D‐glucuronide) (E 2 17βG). On the other hand, some mutations, such as N590A in TM11, D1084N in TM14, and Y1243F in TM17 affected overall activity of the protein. The location of these functionally important residues in TM helices will be discussed in the context of an energy‐minimized model of the membrane‐spanning domains of MRP1 in the presentation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".