Analysis of recombinant tagged equilibrative nucleoside transporter 1 (ENT1) expressed in <i>E. coli</i>This paper is one of a selection of papers published in a Special Issue entitled CSBMCB 53rd Annual Meeting — Membrane Proteins in Health and Disease, and has undergone the Journal’s usual peer review process.
Bibliographic record
Abstract
Nucleoside transporters (NTs) are integral membrane proteins necessary for the cellular entry of nucleoside analog drugs used in chemotherapeutic treatment of conditions such as cancer and viral or parasitic infections. NTs are also the targets of certain drugs used in the treatment of various cardiovascular conditions. Because of the importance of NTs in drug uptake, determination of the three-dimensional structure of these proteins, particularly hENT1, has the potential to improve these treatments through structure-based design of more specifically targeted and transported drugs. In this paper, we use NMR spectroscopy to investigate the structure of the large intracellular loop between transmembrane domains 6 and 7 and we also describe a method for the successful overexpression of full-length hENT1 in a bacterial system. Recombinant tandem histidine-affinity (HAT) and 3×FLAG tagged hENT1 was overexpressed in E. coli, affinity purified, and functionally characterized by nitrobenzylthioinosine (NBTI) binding. Anti-3×FLAG immunodetection confirmed the expression of N-HAT-3×FLAG-hENT1, while increased NBTI binding (3.2-fold compared with controls) confirmed the conformational integrity of the recombinant hENT1 within the bacterial inner membrane. Yields of recombinant hENT1 using this approach were ~15 µg/L of bacterial culture and this approach provides a basis for large-scale production of protein for a variety of purposes.
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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 | 0.001 |
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".