Design and Construction of Recombinant ELP-Intein Cassette for Use in Simple and new Purification Methods of Recombinant Proteins
Bibliographic record
Abstract
Background and Objective: Use of elastin-like proteins (ELPs) provides high-performance protein purification without need for chromatography.In line with cost reduction and facilitation of recombinant proteins purification, which represent a high percentage of production costs, in this project, we eliminated the need for proteases in the process of separation of recombinant proteins from ELP by designing a cassette using ELPs properties as well as insertion of autocatalytic intein protein between the recombinant protein and ELP.Methods: In this study, at first Mxe GyrA intein gene was amplified from pTXB1 vector by PCR method and cloned into pUC57-hEGF vector.Then, 8xELP repetitive sequences were first cloned in pUC57 vector and then into pUC57-intein-hEGF vector in the upstream of Intein-hEGF.Result: The design and construction stages of pUC57-8xELP-Intein-hEGF cassette was successful and the accuracy of 8xELP-Intein-hEGF was confirmed by sequencing.Conclusion: The use of ELP-intein cassette provides recombinant protein purification only with steps consisting of temperature, salt, and centrifugation, without need for proteolytic enzymes, and access to this technology provides the possibility of production and purification of recombinant proteins with minimum cost and facilities.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".