Optimization of elastin‐like polypeptide fusions for expression and purification of recombinant proteins in plants
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it