Production of Recombinant PRMT Proteins using the Baculovirus Expression Vector System
Bibliographic record
Abstract
Protein arginine methyltransferases (PRMTs) methylate arginine residues on a wide variety of proteins that play roles in numerous cellular processes. PRMTs can either mono- or dimethylate arginine guanidino groups symmetrically or asymmetrically. The enzymology of these proteins is a complex and intensely investigated area that requires milligram quantities of high-quality recombinant protein. The baculovirus expression vector system (BEVS) employing Autographa californica multiple nucleopolyhedrovirus (AcMNPV) and Spodoptera frugiperda 9 (Sf9) insect cells has been used for expression screening and production of many PRMTs, including PRMT 1, 2, and 4 through 9. To simultaneously screen for the expression of multiple constructs of these proteins, including domains and truncated fragments as well as the full-length proteins, we have applied scalable methods utilizing adjustable and programmable multichannel pipettes, combined with 24- and 96-well plates and blocks. Overall, these method adjustments enabled a large-scale generation of bacmid DNA, recombinant viruses, and protein expression screening. Using culture vessels with a high-fill volume of Sf9 cell suspension helped to overcome space limitations in the production pipeline for single batch large-scale protein production. Here, we describe detailed protocols for the efficient and cost-effective expression of functional PRMTs for biochemical, biophysical, and structural studies.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.002 |
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".