Arginines in the First Transmembrane Segment Promote Maturation of a P-glycoprotein Processing Mutant by Hydrogen Bond Interactions with Tyrosines in Transmembrane Segment 11
Bibliographic record
Abstract
A key goal is to correct defective folding of mutant ATP binding cassette (ABC) transporters, as they cause diseases such as cystic fibrosis. P-glycoprotein (ABCB1) is a useful model system because introduction of an arginine at position 65 of the first transmembrane (TM) segment could repair folding defects. To determine the mechanism of arginine rescue, we first tested the effects of introducing arginines at other positions in TM1 (residues 52-72) of a P-glycoprotein processing mutant (G251V) that is defective in folding and trafficking to the cell surface (20% maturation efficiency). We found that arginines introduced into one face of the TM1 helix (positions 52, 55, 56, 59, 60, 62, 63, 66, and 67) inhibited maturation, whereas arginines on the opposite face of the helix promoted (positions 64, 65, 68, and 71) or had little effect (positions 61, and 69) on maturation. Arginines at positions 61, 64, 65, and 68 appeared to lie close to the drug binding sites as they reduced the apparent affinity for drug substrates such as vinblastine and verapamil. Therefore, arginines that promoted maturation may face an aqueous drug translocation pathway, whereas those that inhibited maturation may face the lipid bilayer. The highest maturation efficiencies (60-85%) were observed with the Arg-65 and Arg-68 mutants. Mutations that removed hydrogen bond acceptors (Y950F/Y950A or Y953F/Y953A) in TM11 predicted to lie close to Arg-65 or Arg-68 inhibited maturation but did not affect maturation of the G251V parent. Therefore, arginine may rescue defective folding by promoting packing of the TM segments through hydrogen bond interactions.
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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 | 0.001 |
| 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".