Large scale MPro MERS-CoV protein expression setup and operation of Single Use Bubble Column Reactors : Litre-Scale Expression of Recombinant Proteins for Structural Biology and Drug Design (SBDD) v1
Bibliographic record
Abstract
This protocol describes a novel single-use Bubble Column Reactor (suBCR) array system for large-scale recombinant protein production in E. coli, specifically demonstrated with MERS-CoV main protease (MPro). Traditional shake-flask methods are inadequate for meeting the high protein quantities required for structure-based drug discovery (SBDD) workflows, particularly when multiple protein constructs need to be evaluated simultaneously. The suBCR system enables parallel 1-liter E. coli batch cultivation using disposable bioreactor bags arranged in a heated water bath with controlled aeration. The protocol utilizes auto-induction Terrific Broth media supplemented with antifoam, antibiotics, and glycerol. Cultures are inoculated with starter cultures and grown at 37°C for approximately 4 hours until reaching exponential phase, followed by overnight protein expression at ambient temperature (25-27°C) for 16-20 hours. The system achieved successful expression of MERS-CoV MPro protein with high yields, producing 199.5 g wet cell weight from 6 liters total culture volume (OD₆₀₀ = 39.6). Quality control using rapid nickel-magnetic bead purification and SDS-PAGE analysis confirmed successful protein overexpression. The suBCR array addresses the bottleneck of insufficient protein quantities in structural biology pipelines while reducing labor burden and increasing throughput compared to conventional methods. This scalable, cost-effective approach represents a significant advancement in protein production methodology for structural biology and drug discovery applications, offering a practical solution for laboratories requiring multiple protein constructs for crystallographic 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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".