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A review of machine learning methods to predict the solubility of overexpressed recombinant proteins in Escherichia coli.

ABSTRACT:

Background

Over 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.

Results

This 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.

Conclusions

This 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.

SUBMITTER: Habibi N

PROVIDER: S-EPMC4098780 | biostudies-literature | 2014 May

REPOSITORIES: biostudies-literature

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A review of machine learning methods to predict the solubility of overexpressed recombinant proteins in Escherichia coli.

Habibi Narjeskhatoon N Mohd Hashim Siti Z SZ Norouzi Alireza A Samian Mohammed Razip MR

BMC bioinformatics 20140508

<h4>Background</h4>Over 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 accentu ...[more]

PMID: 24885721

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Project description:BACKGROUND: Overexpression 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). RESULTS: Overexpression 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 ?32 was maintained at a higher level following induction. CONCLUSIONS: Differential 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 ?32, a bacterial heat shock transcription factor, at higher levels during overproduction.

Project description:BackgroundThe expression of heterologous proteins in Escherichia coli is strongly affected by codon bias. This phenomenon occurs when the codon usage of the mRNA coding for the foreign protein differs from that of the bacterium. The ribosome pauses upon encountering a rare codon and may detach from the mRNA, thereby the yield of protein expression is reduced. Several bacterial strains have been engineered to overcome this effect. However, the increased rate of translation may lead to protein misfolding and insolubilization. In order to prove this assumption, the solubility of several recombinant proteins from plants was studied in a codon bias-adjusted E. coli strain.ResultsThe expression of eight plant proteins in Escherichia coli BL21(DE3)-pLysS and BL21(DE3)-CodonPlus-pRIL was systematically studied. The CodonPlus strain contains extra copies of the argU, ileY, and leuW tRNA genes, which encode tRNAs that recognize the codons AGA/AGG, AUA and CUA, respectively (RIL codons). The level of expression and solubility of the recombinant proteins were analyzed by means of sodium dodecyl sulfate polyacrylamide gel electrophoresis and Western blotting. We found that for all proteins the solubility was at least 25% in the BL21(DE3)-pLysS strain. However, when expressed in the BL21(DE3)-CodonPlus-pRIL strain, proteins having more than 5% of amino acids coded by RIL codons were localized mainly in the insoluble fraction. Also, their expression caused retarded growth and low cell yield in the codon bias-adjusted strain at all temperatures tested. On the contrary, the solubility of proteins containing less than 5% of amino acids coded by RIL codons remained unchanged in both strains and their expression caused no effect on cell growth.ConclusionOur results show that the expression of heterologous proteins coded by high RIL codon content coding sequences in a codon bias-adjusted strain is detrimental for their solubility. Our data support the hypothesis that the possible elimination of translational pauses that increase translation rate leads to protein misfolding and aggregation. This stresses the importance of strain selection according to codon content in any scheme where a large amount of biologically active product is desirable.

Dataset Information

A review of machine learning methods to predict the solubility of overexpressed recombinant proteins in Escherichia coli.

Background

Results

Conclusions

Publications

A review of machine learning methods to predict the solubility of overexpressed recombinant proteins in Escherichia coli.

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