A Chimeric Affinity Tag for Efficient Expression and Chromatographic Purification of Heterologous Proteins from Plants
Bibliographic record
Abstract
The use of plants as expression hosts for recombinant proteins is an increasingly attractive option for the production of complex and challenging biopharmaceuticals. Tools are needed at present to marry recent developments in high-yielding gene vectors for heterologous expression with routine protein purification techniques. In this study, we designed the Cysta-tag, a new purification tag for immobilized metal affinity chromatography (IMAC) of plant-made proteins based on the protein-stabilizing fusion partner SlCYS8. We show that the Cysta-tag may be used to readily purify proteins under native conditions, and then be removed enzymatically to isolate the protein of interest. We also show that commonly used protease recognition sites for linking purification tags are differentially stable in leaves of the commonly used expression host Nicotiana benthamiana, with those linkers susceptible to cysteine proteases being less stable then serine protease-cleavable linkers. As an example, we describe a Cysta-tag experimental scheme for the one-step purification of a clinically useful protein, human α1-antitrypsin, transiently expressed in N. benthamiana. With potential applicability to the variety of chromatography formats commercially available for IMAC-based protein purification, the Cysta-tag provides a convenient means for the efficient and cost-effective purification of recombinant proteins from plant tissues.
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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".