Affinity‐tagged green fluorescent protein (GFP) extraction from a clarified <i>E. coli</i> cell lysate using a two‐phase aqueous micellar system
Bibliographic record
Abstract
Green fluorescent protein (GFP) has been proposed as an ideal choice for a protein-based biological indicator for use in the validation of decontamination or disinfection treatments. In this article, we present a potentially scalable and cost-effective way to purify recombinant GFP, produced by fermentation in Escherichia coli, by affinity-enhanced extraction in a two-phase aqueous micellar system. Affinity-enhanced partitioning, which improves the specificity and yield of the target protein by specific bioaffinity interactions, has been demonstrated. A novel affinity tag, family 9 carbohydrate-binding module (CBM9) is fused to GFP, and the resulting fusion protein is affinity-extracted in a decyl beta-D-glucopyranoside (C10G1) two-phase aqueous micellar system. In this system, C10G1 acts as phase forming and as affinity surfactant. We will further demonstrate the implementation of this concept to attain partial recovery of affinity-tagged GFP from a clarified E. coli cell lysate, including the simultaneous removal of other contaminating proteins. The cell lysate was partitioned at three levels of dilution (5x, 10x, and 40x). Irrespective of the dilution level, CBM9-GFP was found to partition preferentially to the micelle-rich phase, with the same partition coefficient value as that found in the absence of the cell lysate. The host cell proteins from the cell lysate were found to partition preferentially to the micelle-poor phase, where they experience less excluded-volume interactions. The demonstration of proof-of-principle of the direct affinity-enhanced extraction of CBM9-GFP from the cell lysate represents an important first step towards developing a cost-effective separation method for GFP, and more generally, for other proteins of interest.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".