Optimization of elastin‐like polypeptide fusions for expression and purification of recombinant proteins in plants
Bibliographic record
Abstract
The demand for recombinant proteins for medical and industrial use is expanding rapidly and plants are now recognized as an efficient, inexpensive means of production. Although the accumulation of recombinant proteins in transgenic plants can be low, we have previously demonstrated that fusions with an elastin-like polypeptide (ELP) tag can significantly enhance the production yield of a range of different recombinant proteins in plant leaves. ELPs are biopolymers with a repeating pentapeptide sequence (VGVPG)(n) that are valuable for bioseparation, acting as thermally responsive tags for the non-chromatographic purification of recombinant proteins. To determine the optimal ELP size for the accumulation of recombinant proteins and their subsequent purification, various ELP tags were fused to green fluorescent protein, interleukin-10, erythropoietin and a single chain antibody fragment and then transiently expressed in tobacco leaves. Our results indicated that ELP tags with 30 pentapeptide repeats provided the best compromise between the positive effects of small ELP tags (n = 5-40) on recombinant protein accumulation and the beneficial effects of larger ELP tags (n = 80-160) on recombinant protein recovery during inverse transition cycling (ITC) purification. In addition, the C-terminal orientation of ELP fusion tags produced higher levels of target proteins, relative to N-terminal ELP fusions. Importantly, the ELP tags had no adverse effect on the receptor binding affinity of erythropoietin, demonstrating the inert nature of these tags. The use of ELP fusion tags provides an approach for enhancing the production of recombinant proteins in plants, while simultaneously assisting in their 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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".