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Surface patches on recombinant erythropoietin predict protein solubility: engineering proteins to minimise aggregation.

ABSTRACT: BACKGROUND:Protein solubility characteristics are important determinants of success for recombinant proteins in relation to expression, purification, storage and administration. Escherichia coli offers a cost-efficient expression system. An important limitation, whether for biophysical studies or industrial-scale production, is the formation of insoluble protein aggregates in the cytoplasm. Several strategies have been implemented to improve soluble expression, ranging from modification of culture conditions to inclusion of solubility-enhancing tags. RESULTS:Surface patch analysis has been applied to predict amino acid changes that can alter the solubility of expressed recombinant human erythropoietin (rHuEPO) in E. coli, a factor that has importance for both yield and subsequent downstream processing of recombinant proteins. A set of rHuEPO proteins (rHuEPO E13K, F48D, R150D, and F48D/R150D) was designed (from the framework of wild-type protein, rHuEPO WT, via amino acid mutations) that varied in terms of positively-charged patches. A variant predicted to promote aggregation (rHuEPO E13K) decreased solubility significantly compared to rHuEPO WT. In contrast, variants predicted to diminish aggregation (rHuEPO F48D, R150D, and F48D/R150D) increased solubility up to 60% in relation to rHuEPO WT. CONCLUSIONS:These findings are discussed in the wider context of biophysical calculations applied to the family of EPO orthologues, yielding a diverse range of calculated values. It is suggested that combining such calculations with naturally-occurring sequence variation, and 3D model generation, could lead to a valuable tool for protein solubility design.

SUBMITTER: Carballo-Amador MA

PROVIDER: S-EPMC6507049 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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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.

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Surface patches on recombinant erythropoietin predict protein solubility: engineering proteins to minimise aggregation.

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