Dataset Information

Single-Input Regulatory Cascade for in vivo Removal of the Solubility Tag in Fusion Recombinant Proteins Produced by Escherichia coli.

ABSTRACT: Solubility tags are commonly fused to target recombinant proteins to enhance their solubility and stability. In general, these protein tags must be removed to avoid misfolding of the partner protein and to allow for downstream applications. Nevertheless, in vitro tag removal increases process complexity and costs. Herein, we describe a synthetic biology-based strategy to permit in vivo removal of a solubility tag (EDA, KDPG aldolase), through co-expression of the fusion recombinant protein (EDA-EGFP) and the tag-cleaving protease (TEVp), in a controlled manner. Basically, the system uses three repressor proteins (LacI, cI434, and TetR) to regulate the expressions of EDA-EGFP and TEVp, in a regulatory cascade that culminates with the release of free soluble target protein (EGFP), following a single chemical induction by IPTG. The system worked consistently when all biological parts were cloned in a single plasmid, pSolubility(SOL)A (7.08 Kb, AmpR), and transformed in Escherichia coli Rosetta (DE3) or BL21(DE3) strains. Total soluble recombinant protein yield (EDA-EGFP + free EGFP) was ca. 272.0 ± 60.1 ?g/mL of culture, following IMAC purification; free EGFP composed great part (average = 46.5%; maximum = 67.3%) of the total purified protein fraction and was easily separated from remaining fusion EDA-EGFP (53 KDa) through filtration using a 50 KDa cut-off centrifugal filter.

SUBMITTER: Silva FSR

PROVIDER: S-EPMC6710347 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Single-Input Regulatory Cascade for in vivo Removal of the Solubility Tag in Fusion Recombinant Proteins Produced by Escherichia coli.

Silva Filipe S R FSR Santos Sara P O SPO Meyer Roberto R Alcantara-Neves Neuza M NM Pinheiro Carina S CS Pacheco Luis G C LGC

Frontiers in bioengineering and biotechnology 20190820

Solubility tags are commonly fused to target recombinant proteins to enhance their solubility and stability. In general, these protein tags must be removed to avoid misfolding of the partner protein and to allow for downstream applications. Nevertheless, in vitro tag removal increases process complexity and costs. Herein, we describe a synthetic biology-based strategy to permit in vivo removal of a solubility tag (EDA, KDPG aldolase), through co-expression of the fusion recombinant ...[more]

PMID: 31482090

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Project description:BackgroundOverexpression of recombinant proteins usually triggers the induction of heat shock proteins that regulate aggregation and solubility of the overexpressed protein. The two-dimensional gel electrophoresis (2-DE)-mass spectrometry approach was used to profile the proteome of Escherichia coli overexpressing N-acetyl-D-glucosamine 2-epimerase (GlcNAc 2-epimerase) and N-acetyl-D-neuraminic acid aldolase (Neu5Ac aldolase), both fused to glutathione S-transferase (GST) and polyionic peptide (5D or 5R).ResultsOverexpression of fusion proteins by IPTG induction caused significant differential expression of numerous cellular proteins; most of these proteins were down-regulated, including enzymes connected to the pentose phosphate pathway and the enzyme LuxS that could lead to an inhibition of tRNA synthesis. Interestingly, when plasmid-harboring cells were cultured in LB medium, gluconeogenesis occurred mainly through MaeB, while in the host strain, gluconeogenesis occurred by a different pathway (by Mdh and PckA). Significant up-regulation of the chaperones ClpB, HslU and GroEL and high-level expression of two protective small heat shock proteins (IbpA and IbpB) were found in cells overexpressing GST-GlcNAc 2-epimerase-5D but not in GST-Neu5Ac aldolase-5R-expressing E. coli. Although most of the recombinant protein was present in insoluble aggregates, the soluble fraction of GST-GlcNAc 2-epimerase-5D was higher than that of GST-Neu5Ac aldolase-5R. Also, in cells overexpressing recombinant GST-GlcNAc 2-epimerase-5D, the expression of σ32 was maintained at a higher level following induction.ConclusionsDifferential expression of metabolically functional proteins, especially those in the gluconeogenesis pathway, was found between host and recombinant cells. Also, the expression patterns of chaperones/heat shock proteins differed among the plasmid-harboring bacteria in response to overproduction of recombinant proteins. In conclusion, the solubility of overexpressed recombinant proteins could be enhanced by maintaining the expression of σ32, a bacterial heat shock transcription factor, at higher levels during overproduction.

Project description:BackgroundOver the last 20 years in biotechnology, the production of recombinant proteins has been a crucial bioprocess in both biopharmaceutical and research arena in terms of human health, scientific impact and economic volume. Although logical strategies of genetic engineering have been established, protein overexpression is still an art. In particular, heterologous expression is often hindered by low level of production and frequent fail due to opaque reasons. The problem is accentuated because there is no generic solution available to enhance heterologous overexpression. For a given protein, the extent of its solubility can indicate the quality of its function. Over 30% of synthesized proteins are not soluble. In certain experimental circumstances, including temperature, expression host, etc., protein solubility is a feature eventually defined by its sequence. Until now, numerous methods based on machine learning are proposed to predict the solubility of protein merely from its amino acid sequence. In spite of the 20 years of research on the matter, no comprehensive review is available on the published methods.ResultsThis paper presents an extensive review of the existing models to predict protein solubility in Escherichia coli recombinant protein overexpression system. The models are investigated and compared regarding the datasets used, features, feature selection methods, machine learning techniques and accuracy of prediction. A discussion on the models is provided at the end.ConclusionsThis study aims to investigate extensively the machine learning based methods to predict recombinant protein solubility, so as to offer a general as well as a detailed understanding for researches in the field. Some of the models present acceptable prediction performances and convenient user interfaces. These models can be considered as valuable tools to predict recombinant protein overexpression results before performing real laboratory experiments, thus saving labour, time and cost.

Dataset Information

Single-Input Regulatory Cascade for in vivo Removal of the Solubility Tag in Fusion Recombinant Proteins Produced by Escherichia coli.

Publications

Single-Input Regulatory Cascade for <i>in vivo</i> Removal of the Solubility Tag in Fusion Recombinant Proteins Produced by <i>Escherichia coli</i>.

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