Purification of an elastin‐like fusion protein by microfiltration
Bibliographic record
Abstract
This article describes a simple and potentially scalable microfiltration method for purification of recombinant proteins. This method is based on the fact that when an elastin-like polypeptide (ELP) is fused to a target protein, the inverse phase transition behavior of the ELP tag is imparted to the fusion protein. Triggering the phase transition of a solution of the ELP fusion protein by an increase in temperature, or isothermally by an increase in salt concentration, results in the formation of micron-sized aggregates of the ELP fusion protein. In this article, it is shown that these aggregates are efficiently retained by a microfiltration membrane, while contaminating E. coli proteins passed through the membrane upon washing. Upon reversing the phase transition by flow of Milli-Q water, soluble, pure, and functionally active protein is eluted from the membrane. Proof-of principle of this approach was demonstrated by purifying a fusion of thioredoxin with ELP (Trx-ELP) with greater than 95% recovery of protein and with greater than 95% purity (as estimated from SDS-PAGE gels). The simplicity of this method is demonstrated for laboratory scale purification by purifying Trx-ELP from cell lysate using a syringe and a disposable microfiltration cartridge. The potential scalability of this purification as an automated, continuous industrial-scale process is also demonstrated using a continuous stirred cell equipped with a microfiltration membrane.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".