Bioinformatics approaches for improved recombinant protein production in Escherichia coli: protein solubility prediction

Abstract

The solubility of recombinant protein expressed in Escherichia coli often represents the production yield. However, up-to-date, instances of successful production of soluble recombinant proteins in E. coli expression system with high yield remain scarce. This is mainly due to the difficulties in improving the overall production capacity, as most of the well-established strategies usually involve a series of trial and error steps with unguaranteed success. One way to concurrently improve the production yield and minimize the production cost would be incorporating the potency of bioinformatics tools to conduct in silico studies, which forecasts the outcome before actual experimental work. In this article, we review and compare seven prediction tools available, which predict the solubility of protein expressed in E. coli, using the following criteria: prediction performance, usability, utility, prediction tool development and validation methodologies. This comprehensive review will be a valuable resource for researchers with limited prior experience in bioinformatics tools. As such, this will facilitate their choice of appropriate tools for studies related to enhancement of intracellular recombinant protein production in E. coli.

Prediction of recombinant protein overexpression in Escherichia coli using a machine learning based model (RPOLP)

“…size of the recombinant gene, composition of nucleotides), but due to the existence of several parameters and their complicated relationships, which are partially known, it is not possible to know the expected level of overexpression in advance; thus necessitating a resource intensive "trial and error" approach. The provision of a tool to conduct theoretical prediction of protein overexpression level will facilitate the development of large-scale proteomics studies [4]. To the best of our knowledge, no previous study has addressed the problem of recombinant protein overexpression level prediction.…”

Prediction of soluble heterologous protein expression levels inEscherichia colifrom sequence-based features and its potential in biopharmaceutical process development

“…Common strategies include using weak promoters and reducing E. coli culture temperature [15,16], coexpression with molecular chaperones [17,18], fusion with solubility enhancing tags [19], site-directed molecular evolution method [20] and structure-guided molecular mutagenesis if a 3D structure is available [21]. These efforts are actually very time consuming, costly and the success rate has also been relatively low [22]. In addition, they mainly concentrated on the host cells themselves at the levels of genetic manipulation or optimization of critical process parameters.…”

Correlation between Protein Primary Structure and Soluble Expression Level of HSA dAb in Escherichia coli

“…Several prediction models have been established (6,9), such as the Harrison prediction model (10), multiple linear regression (MLR) model (11), solubility index-based model (12), support vector machine-based model (13,14), PROSO model (15), SOLpro model (16), cc SOL model (17) and PROSO II model (18). These bioinformatics models can significantly reduce trial and error procedures involved in optimization of expression systems to increase the soluble expression level of heterologous proteins.…”

“…These bioinformatics models can significantly reduce trial and error procedures involved in optimization of expression systems to increase the soluble expression level of heterologous proteins. However, there has been limited application of these prediction models, partly because of the significant differences among the proteins chosen for building them and also because of the adoption of inconsistent culture conditions for expression of proteins (6,8,9). Domain antibodies (dAbs), which consist of only variable regions of heavy (V H ) or light (V L ) chains (19), have simple tertiary structures ( Fig.…”