Bioinformatics approaches for improved recombinant protein production in Escherichia coli: protein solubility prediction.
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.
Catherine Ching Han
CC
Chang
Jiangning
J
Song
Beng Ti
BT
Tey
Ramakrishnan Nagasundara
RN
Ramanan
D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D016208:Databases, Factual; D004926:Escherichia coli / Q000235:genetics / Q000378:metabolism*; D011994:Recombinant Proteins / Q000096:biosynthesis* / Q000737:chemistry* / Q000235:genetics; D012984:Software; D012995:Solubility
D011994:Recombinant Proteins
heterologous expression; in silico prediction; intracellular expression; machine learning algorithm; prediction model; protein solubility