An in silico overview on the usefulness of tags and linkers in plant molecular pharming
Bibliographic record
Abstract
Plant molecular pharming is a promising concept based on the large-scale production of recombinant proteins encompassing antibodies, vaccines and enzymes for human or veterinary uses and treatments. This new branch of biopharmaceutical industry offers pratcical and safety advantages over other traditional production systems. In higher plants, the complex cellular machinery makes possible synthesis and posttranslational modifications of heterologous protein macromolecules. The limiting obstacle to using this plant system at industrial scale is most often the low yield of the recombinant proteins. To improve this production level, many studies have been focusing on the choice of plant species, tissues, organs and cell suspension cultures or various upstream and downstream constituents in the expression cassettes. Likewise, new engineering technologies in plant molecular pharming have emerged relying on the usefulness of using soybean agglutinin (SBA), hydrophobin, zein and elastin-like peptide tags which are employed to extract and purify recombinant proteins in some host systems and under the control, and as a part, of different expression cassettes. Known to be very useful tools in recombinant proteins linkers separate different domains or units of the heterologous gene and thereby keep the functionality of the protein of interest. Here, we compare computationally one tag SBA as a part of fusion with a pharmaceutical human protein ADA joint directly or by the specific flexible (GGGGS)3 liker. The in silico analysis focuses on the mRNAs stability and fusions of tagged and tagged-linked ADA recombinant proteins.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".