GST-His purification: A Two-step Affinity Purification Protocol Yielding Full-length Purified Proteins
Bibliographic record
Abstract
Key assays in enzymology for the biochemical characterization of proteins in vitro necessitate high concentrations of the purified protein of interest. Protein purification protocols should combine efficiency, simplicity and cost effectiveness. Here, we describe the GST-His method as a new small-scale affinity purification system for recombinant proteins, based on a N-terminal Glutathione Sepharose Tag (GST) and a C-terminal 10xHis tag, which are both fused to the protein of interest. The latter construct is used to generate baculoviruses, for infection of Sf9 infected cells for protein expression. GST is a rather long tag (29 kDa) which serves to ensure purification efficiency. However, it might influence physiological properties of the protein. Hence, it is subsequently cleaved off the protein using the PreScission enzyme. In order to ensure maximum purity and to remove the cleaved GST, we added a second affinity purification step based on the comparatively small His-Tag. Importantly, our technique is based on two different tags flanking the two ends of the protein, which is an efficient tool to remove degraded proteins and, therefore, enriches full-length proteins. The method presented here does not require an expensive instrumental setup, such as FPLC. Additionally, we incorporated MgCl2 and ATP washes to remove heat shock protein impurities and nuclease treatment to abolish contaminating nucleic acids. In summary, the combination of two different tags flanking the N- and the C-terminal and the capability to cleave off one of the tags, guaranties the recovery of a highly purified and full-length protein of interest.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.009 |
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".