Prokaryotic Ubiquitin-Like ThiS Fusion Enhances the Heterologous Protein Overexpression and Aggregation in Escherichia coli
Post-publication record
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Bibliographic record
Abstract
Fusion tags are commonly employed to enhance target protein expression, improve their folding and solubility, and reduce protein degradation in expression of recombinant proteins. Ubiquitin (Ub) and SUMO are highly conserved small proteins in eukaryotes, and frequently used as fusion tags in prokaryotic expression. ThiS, a smaller sulfur-carrier protein involved in thiamin synthesis, is conserved among most prokaryotic species. The structural similarity between ThiS and Ub provoked us into expecting that the former could be used as a fusion tag. Hence, ThiS was fused to insulin A and B chains, murine Ribonuclease Inhibitor (mRI) and EGFP, respectively. When induced in Escherichia coli, ThiS-fused insulin A and B chains were overexpressed in inclusion bodies, and to higher levels in comparison to the same proteins fused with Ub. On the contrast, ThiS fusion of mRI, an unstable protein, resulted in enhanced degradation that was not alleviated in protease-deficient strains. While the degradation of Ub- and SUMO-fused mRI was less and seemed protease-dependent. Enhanced degradation of mRI did not occur for the fusions with half-molecules of ThiS. When ThiS-tag was fused to the C-terminus of EGFP, higher expression, predominantly in inclusion bodies, was observed again. It was further found that ThiS fusion of EGFP significantly retarded its refolding process. These results indicated that prokaryotic ThiS is able to promote the expression of target proteins in E. coli, but enhanced degradation may occur in case of unstable targets. Unlike eukaryotic Ub-based tags usually increase the solubility and folding of proteins, ThiS fusion enhances the expression by augmenting the formation of inclusion bodies, probably through retardation of the folding of target proteins.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".