Bibliographic record
Abstract
Solute carriers (SLCs) are membrane transporters that import and export a range of endogenous and exogenous substrates, including ions, nutrients, metabolites, neurotransmitters, and pharmaceuticals. Despite having emerged as attractive therapeutic targets and markers of disease, this group of proteins is still relatively underdrugged by current pharmaceuticals. Drug discovery projects for these transporters are impeded by limited structural, functional, and physiological knowledge, ultimately due to the difficulties in the expression and purification of this class of membrane-embedded proteins. Here, we demonstrate methods to obtain high-purity, milligram quantities of human SLC transporter proteins using codon-optimized gene sequences. In conjunction with a systematic exploration of construct design and high-throughput expression, these protocols ensure the preservation of the structural integrity and biochemical activity of the target proteins. We also highlight critical steps in the eukaryotic cell expression, affinity purification, and size-exclusion chromatography of these proteins. Ultimately, this workflow yields pure, functionally active, and stable protein preparations suitable for high-resolution structure determination, transport studies, small-molecule engagement assays, and high-throughput in vitro screening.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.501 | 0.315 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".