ESPRESSO: A system for estimating protein expression and solubility in protein expression systems

Abstract

Recombinant protein technology is essential for conducting protein science and using proteins as materials in pharmaceutical or industrial applications. Although obtaining soluble proteins is still a major experimental obstacle, knowledge about protein expression/solubility under standard conditions may increase the efficiency and reduce the cost of proteomics studies. In this study, we present a computational approach to estimate the probability of protein expression and solubility for two different protein expression systems: in vivo Escherichia coli and wheat germ cell-free, from only the sequence information. It implements two kinds of methods: a sequence/predicted structural property-based method that uses both the sequence and predicted structural features, and a sequence pattern-based method that utilizes the occurrence frequencies of sequence patterns. In the benchmark test, the proposed methods obtained F-scores of around 70%, and outperformed publicly available servers. Applying the proposed methods to genomic data revealed that proteins associated with translation or transcription have a strong tendency to be expressed as soluble proteins by the in vivo E. coli expression system. The sequence pattern-based method also has the potential to indicate a candidate region for modification, to increase protein solubility. All methods are available for free at the ESPRESSO server (http://mbs.cbrc.jp/ESPRESSO).