Protein body‐inducing fusions for high‐level production and purification of recombinant proteins in plants
Bibliographic record
Abstract
For the past two decades, therapeutic and industrially important proteins have been expressed in plants with varying levels of success. The two major challenges hindering the economical production of plant-made recombinant proteins include inadequate accumulation levels and the lack of efficient purification methods. To address these limitations, several fusion protein strategies have been recently developed to significantly enhance the production yield of plant-made recombinant proteins, while simultaneously assisting in their subsequent purification. Elastin-like polypeptides are thermally responsive biopolymers composed of a repeating pentapeptide 'VPGXG' sequence that are valuable for the purification of recombinant proteins. Hydrophobins are small fungal proteins capable of altering the hydrophobicity of their respective fusion partner, thus enabling efficient purification by surfactant-based aqueous two-phase systems. Zera, a domain of the maize seed storage protein γ-zein, can induce the formation of protein storage bodies, thus facilitating the recovery of fused proteins using density-based separation methods. These three novel protein fusion systems have also been shown to enhance the accumulation of a range of different recombinant proteins, while concurrently inducing the formation of protein bodies. The packing of these fusion proteins into protein bodies may exclude the recombinant protein from normal physiological turnover. Furthermore, these systems allow for quick, simple and inexpensive nonchromatographic purification of the recombinant protein, which can be scaled up to industrial levels of protein production. This review will focus on the similarities and differences of these artificial storage organelles, their biogenesis and their implication for the production of recombinant proteins in plants and their subsequent purification.
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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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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".