SPI “sandwich”: Combined <scp>SUMO‐Peptide‐Intein</scp> expression system and isolation procedure for improved stability and yield of peptides
Bibliographic record
Abstract
Abstract Recombinant peptide production in Escherichia coli is often accomplished through cloning and expression of a fusion protein. The fusion protein partner generally has two requirements: (a) it contains an affinity tag to assist with purification and (b) it can be cleaved off to leave only the desired peptide sequence behind. Common soluble fusion partners include small ubiquitin‐like modifier protein (SUMO), maltose‐binding protein (MBP), glutathione S‐transferase (GST), or intein proteins. However, heterologously expressed peptides can suffer from proteolytic degradation or instability. This degradation can pose a major issue for applications requiring a large amount of purified peptide, such as NMR structural assignments or biochemical assays. Improving peptide yield by testing various expression and isolation conditions requires a significant amount of effort and may not lead to improved results. Here, we cloned and expressed four different peptides as SUMO fusion proteins. These peptides (lactococcin A, leucocin A, faerocin MK, neopetrosiamide A) were truncated during expression and isolation as SUMO fusions, resulting in low yields of purified peptide. To prevent this degradation and improve yield, we designed a new expression system to create a “sandwiched” fusion protein of the form: His6‐SUMO‐peptide‐intein (SPI). These sandwiched peptides were more stable and protected against degradation, resulting in improved yields (up to 17‐fold) under a set of standard expression and isolation procedures. This SPI expression system uses only two commercially available vectors and standard protein purification techniques, and therefore may offer an economical and facile route to improve yields for peptides that undergo degradation.
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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 | 0.000 | 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".