Methods for Recombinant Production and Purification of Peptides as SUMO‐Peptide‐Intein Fusion Proteins to Protect from Degradation
Bibliographic record
Abstract
Heterologous expression in Escherichia coli is a commonly used method to produce ribosomally synthesized peptides for further study. This generally requires expression of the target protein with an affinity fusion tag, followed by isolation of the fusion protein from a cellular lysate by affinity purification, and finally by removal of the fusion tag and purification of the desired peptide. Sometimes, however, fusion proteins may be degraded during recombinant expression in E. coli. We recently reported an expression system that sandwiches the target peptide between an N-terminal small ubiquitin-like modifier (SUMO) protein and a C-terminal intein. This SUMO-peptide-intein (SPI) fusion protein protects the central peptide from degradation and can lead to improved peptide yield after purification. In this report, we detail the cloning, expression, and isolation procedures for the SPI fusion system, with comments on conditions that can be optimized for different peptides to obtain maximal yield for each construct. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Cloning to construct SPI gene Basic Protocol 2: Expression of SPI fusion proteins in E. coli BL21(DE3) Support Protocol: Optimization of expression and induction conditions Basic Protocol 3: Isolation and purification of SPI fusion proteins with a chitin column Alternate Protocol: Isolation and purification of SPI fusion proteins without chitin.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.022 |
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".