Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Highly Accessed Research article

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

Narjeskhatoon Habibi1*, Siti Z Mohd Hashim1, Alireza Norouzi1 and Mohammed Razip Samian234

Author Affiliations

1 Faculty of Computing, Universiti Teknologi Malaysia, Johor, Malaysia

2 School of Biological Sciences, Universiti Sains Malaysia, Penang, Malaysia

3 Advanced Medical and Dental Institute, Universiti Sains Malaysia, Penang, Malaysia

4 Centre for Chemical Biology, Universiti Sains Malaysia, Penang, Malaysia

For all author emails, please log on.

BMC Bioinformatics 2014, 15:134  doi:10.1186/1471-2105-15-134

Published: 8 May 2014



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.


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.


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.

Protein solubility; Protein solubility prediction; In silico prediction; Recombinant protein expression; Escherichia coli; Machine learning; Bioinformatics; Computational biology