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.
Recombinant protein production is a significant biotechnological process as it allows researchers to produce a specific protein in desired quantities. Escherichia coli (E. coli) is the most popular heterologous expression host for the production of recombinant proteins due to its advantages such as low cost, high-productivity, well-characterized genetics, simple growth requirements and rapid growth. There are a number of factors that influence the expression level of a recombinant protein in E. coli which are the gene to be expressed, the expression vector, the expression host, and the culture condition. The major motivation to develop our database, EcoliOverExpressionDB, is to provide a means for researchers to quickly locate key factors in the overexpression of certain proteins. Such information would be a useful guide for the overexpression of similar proteins in E. coli. To the best of the present researchers' knowledge, in general and specifically in E. coli, EcoliOverExpressionDB is the first database of recombinant protein expression experiments which gathers the influential parameters on protein overexpression and the results in one place.
Recombinant protein overexpression, an important biotechnological process, is ruled by complex biological rules which are mostly unknown, is in need of an intelligent algorithm so as to avoid resource-intensive lab-based trial and error experiments in order to determine the expression level of the recombinant protein. The purpose of this study is to propose a predictive model to estimate the level of recombinant protein overexpression for the first time in the literature using a machine learning approach based on the sequence, expression vector, and expression host. The expression host was confined to Escherichia coli which is the most popular bacterial host to overexpress recombinant proteins. To provide a handle to the problem, the overexpression level was categorized as low, medium and high. A set of features which were likely to affect the overexpression level was generated based on the known facts (e.g. gene length) and knowledge gathered from related literature. Then, a representative sub-set of features generated in the previous objective was determined using feature selection techniques. Finally a predictive model was developed using random forest classifier which was able to adequately classify the multi-class imbalanced small dataset constructed. The result showed that the predictive model provided a promising accuracy of 80% on average, in estimating the overexpression level of a recombinant protein.