High‐efficiency recombinant protein purification using <scp>mCherry</scp> and <scp>YFP</scp> nanobody affinity matrices
Bibliographic record
Abstract
Mammalian cell lines are important expression systems for large proteins and protein complexes, particularly when the acquisition of post-translational modifications in the protein's native environment is desired. However, low or variable transfection efficiencies are challenges that must be overcome to use such an expression system. Expression of recombinant proteins as a fluorescent protein fusion enables real-time monitoring of protein expression, and also provides an affinity handle for one-step protein purification using a suitable affinity reagent. Here, we describe a panel of anti-GFP and anti-mCherry nanobody affinity matrices and their efficacy for purification of GFP/YFP or mCherry fusion proteins. We define the molecular basis by which they bind their target proteins using X-ray crystallography. From these analyses, we define an optimal pair of nanobodies for purification of recombinant protein tagged with GFP/YFP or mCherry, and demonstrate these nanobody-sepharose supports are stable to many rounds of cleaning and extended incubation in denaturing conditions. Finally, we demonstrate the utility of the mCherry-tag system by using it to purify recombinant human topoisomerase 2α expressed in HEK293F cells. The mCherry-tag and GFP/YFP-tag expression systems can be utilized for recombinant protein expression individually or in tandem for mammalian protein expression systems where real-time monitoring of protein expression levels and a high-efficiency purification step is needed.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".