Tomato cystatin <i><scp>S</scp>l</i><scp>CYS</scp>8 as a stabilizing fusion partner for human serpin expression in plants
Bibliographic record
Abstract
Studies have reported the usefulness of fusion proteins to bolster recombinant protein yields in plants. Here, we assess the potential of tomato SlCYS8, a Cys protease inhibitor of the cystatin protein superfamily, as a stabilizing fusion partner for human alpha-1-antichymotrypsin (α1ACT) targeted to the plant cell secretory pathway. Using the model expression platform Nicotiana benthamiana, we show that the cystatin imparts a strong stabilizing effect when expressed as a translational fusion with α1ACT, allowing impressive accumulation yields of over 2 mg/g of fresh weight tissue for the human serpin, a 25-fold improvement on the yield of α1ACT expressed alone. Natural and synthetic peptide linkers inserted between SlCYS8 and α1ACT have differential effects on protease inhibitory potency of the two protein partners in vitro. They also have a differential impact on the yield of α1ACT, dependent on the extent to which the hybrid protein may remain intact in the plant cell environment. The stabilizing effect of SlCYS8 does not involve Cys protease inhibition and can be partly reproduced in the cytosol, where peptide linkers are less susceptible to degradation. The effect of SlCYS8 on α1ACT yields could be explained by: (i) an improved translation of the human protein coding sequence; and/or (ii) an overall stabilization of its tertiary structure preventing proteolytic degradation and/or polymerization. These findings suggest the potential of plant cystatins as stabilizing fusion partners for recombinant proteins in plant systems. They also underline the need for an empirical assessment of peptide linker functions in plant cell environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".