A rapid and universal tandem‐purification strategy for recombinant proteins
Bibliographic record
Abstract
A major goal in the production of therapeutic proteins, subunit vaccines, as well as recombinant proteins needed for structure determination and structural proteomics is their recovery in a pure and functional state using the simplest purification procedures. Here, we report the design and use of a novel tandem (His)(6)-calmodulin (HiCaM) fusion tag that combines two distinct purification strategies, namely, immobilized metal affinity (IMAC) and hydrophobic interaction chromatography (HIC), in a simple two-step procedure. Two model constructs were generated by fusing the HiCaM purification tag to the N terminus of either the enhanced green fluorescent protein (eGFP) or the human tumor suppressor protein p53. These fusion constructs were abundantly expressed in Escherichia coli and rapidly purified from cleared lysates by tandem IMAC/HIC to near homogeneity under native conditions. Cleavage at a thrombin recognition site between the HiCaM-tag and the constructs readily produced untagged, functional versions of eGFP and human p53 that were >97% pure. The HiCaM purification strategy is rapid, makes use of widely available, high-capacity, and inexpensive matrices, and therefore represents an excellent approach for large-scale purification of recombinant proteins as well as small-scale protein array designs.
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 | 0.000 |
| 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 | 0.002 |
| 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".