Introducing a New Method for Purification of Human IL-4 by Substitution of a Single Amino Acid in IL-4 Protein Sequence.
Bibliographic record
Abstract
BACKGROUND: It is advantageous to develop an effective purification procedure to produce recombinant protein drugs (rPDs) without any tags. To remove N- or C-terminus tags from the rPDs, several cleavage site-based endopeptidases were used. Separating the endopeptidase enzyme from the rPDs is a time-consuming and costly process. OBJECTIVE: To design and develop a new method for the purification of human interleukin (IL)-4 with potential application for other cytokines. METHODS: Met-like amino acids were substituted at position 120 to reduce the possibility of alteration in the structure of IL-4 and its biological activity. Based on the in silico analysis, isoleucine was chosen as an alternative amino acid, and the M120I mutant IL-4 (mIL-4) model was selected for the downstream analysis. Recombinant mIL-4 was produced in the E.coli BL21 host and purified with CNBr. Then in vitro evaluations of the native and mutant IL-4 were performed. RESULTS: The results showed that both the native and mutant IL-4 had the same effect on TF-1 cell proliferation. On the other hand, there was no significant difference between the effects of native IL-4 (nIL-4) and mIL-4 on the expression of IL-4 and IL-10 in activated peripheral blood mononuclear cells. Native and mutant IL-4 have similar biological activities. CONCLUSION: Here, an efficient and straightforward system is introduced to purify IL-4 cytokine using CNBr, which could be applied to other rPDs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".