PeptiQuick, a One-Step Incorporation of Membrane Proteins into Biotinylated Peptidiscs for Streamlined Protein Binding Assays
Bibliographic record
Abstract
Membrane proteins, including transporters, channels, and receptors, constitute nearly one-fourth of the cellular proteome and over half of current drug targets. Yet, a major barrier to their characterization and exploitation in academic or industrial settings is that most biochemical, biophysical, and drug screening strategies require these proteins to be in a water-soluble state. Our laboratory recently developed the peptidisc, a membrane mimetic offering a "one-size-fits-all" approach to the problem of membrane protein solubility. We present here a streamlined protocol that combines protein purification and peptidisc reconstitution in a single chromatographic step. This workflow, termed PeptiQuick, allows for bypassing dialysis and incubation with polystyrene beads, thereby greatly reducing exposure to detergent, protein denaturation, and sample loss. When PeptiQuick is performed with biotinylated scaffolds, the preparation can be directly attached to streptavidin-coated surfaces. There is no need to biotinylate or modify the membrane protein target. PeptiQuick is showcased here with the membrane receptor FhuA and antimicrobial ligand colicin M, using biolayer interferometry to determine the precise kinetics of their interaction. It is concluded that PeptiQuick is a convenient way to prepare and analyze membrane protein-ligand interactions within one day in a detergent-free environment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".