Maltose-Binding Protein (MBP), a Secretion-Enhancing Tag for Mammalian Protein Expression Systems
Bibliographic record
Abstract
Recombinant proteins are commonly expressed in eukaryotic expression systems to ensure the formation of disulfide bridges and proper glycosylation. Although many proteins can be expressed easily, some proteins, sub-domains, and mutant protein versions can cause problems. Here, we investigated expression levels of recombinant extracellular, intracellular as well as transmembrane proteins tethered to different polypeptides in mammalian cell lines. Strikingly, fusion of proteins to the prokaryotic maltose-binding protein (MBP) generally enhanced protein production. MBP fusion proteins consistently exhibited the most robust increase in protein production in comparison to commonly used tags, e.g., the Fc, Glutathione S-transferase (GST), SlyD, and serum albumin (ser alb) tag. Moreover, proteins tethered to MBP revealed reduced numbers of dying cells upon transient transfection. In contrast to the Fc tag, MBP is a stable monomer and does not promote protein aggregation. Therefore, the MBP tag does not induce artificial dimerization of tethered proteins and provides a beneficial fusion tag for binding as well as cell adhesion studies. Using MBP we were able to secret a disease causing laminin β2 mutant protein (congenital nephrotic syndrome), which is normally retained in the endoplasmic reticulum. In summary, this study establishes MBP as a versatile expression tag for protein production in eukaryotic expression systems.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".