The P-Glycoprotein (ABCB1) Linker Domain Encodes High-Affinity Binding Sequences to α- and β-Tubulins
Bibliographic record
Abstract
P-Glycoprotein (or ABCB1) has been shown to cause multidrug resistance in tumor cell lines selected with lipophilic anticancer drugs. ABCB1 encodes a duplicated molecule with two hydrophobic and hydrophilic domains linked by a highly charged region of approximately 90 amino acids, the "linker domain" with as yet unknown function(s). In this report, we demonstrate a role for this domain in binding to other cellular proteins. Using overlapping hexapeptides that encode the entire amino acid sequence of the linker domain of human ABCB1, we show a direct and specific binding between sequences in the linker domain and several intracellular proteins. Three different polypeptide sequences [617EKGIYFKLVTM627 (LDS617-627), 657SRSSLIRKRSTRRSVRGSQA676 (LDS657-676), and 693PVSFWRIMKLNLT705 (LDS693-705)] in the linker domain interacted tightly with several proteins with apparent molecular masses of approximately 80, 57, and 30 kDa. Interestingly, only the 57 kDa protein (or P57) interacted with all three different sequences of the linker domain. Purification and partial N-terminal amino acid sequencing of P57 showed that it encodes the N-terminal amino acids of alpha- and beta-tubulins. The identity of the P57 interacting protein as tubulins was further confirmed by Western blotting using monoclonal antibodies to alpha- and beta-tubulin. Taken together, the results of this study provide the first evidence for ABCB1 protein interaction mediated by sequences in the linker domain. These findings are likely to provide further insight into the functions of ABCB1 in normal and drug resistant tumor cells.
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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 | 0.001 |
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".