title: Periscope: quantitative prediction of soluble protein expression in the periplasm of Escherichia coli.
abstract: Periplasmic expression of soluble proteins in Escherichia coli not only offers a much-simplified downstream purification process, but also enhances the probability of obtaining correctly folded and biologically active proteins. Different combinations of signal peptides and target proteins lead to different soluble protein expression levels, ranging from negligible to several grams per litre. Accurate algorithms for rational selection of promising candidates can serve as a powerful tool to complement with current trial-and-error approaches. Accordingly, proteomics studies can be conducted with greater efficiency and cost-effectiveness. Here, we developed a predictor with a two-stage architecture, to predict the real-valued expression level of target protein in the periplasm. The output of the first-stage support vector machine (SVM) classifier determines which second-stage support vector regression (SVR) classifier to be used. When tested on an independent test dataset, the predictor achieved an overall prediction accuracy of 78% and a Pearson's correlation coefficient (PCC) of 0.77. We further illustrate the relative importance of various features with respect to different models. The results indicate that the occurrence of dipeptide glutamine and aspartic acid is the most important feature for the classification model. Finally, we provide access to the implemented predictor through the Periscope webserver, freely accessible at http://lightning.med.monash.edu/periscope/.
authors.affiliation: Chemical Engineering Discipline, School of Engineering, Monash University, Jalan Lagoon Selatan 46150, Bandar Sunway, Selangor, Malaysia.|Department of Biochemistry and Molecular Biology, Monash University, Melbourne VIC 3800, Australia.
authors.forename: Catherine Ching Han
authors.initials: CC
authors.lastname: Chang
authors.affiliation: Department of Biochemistry and Molecular Biology, Monash University, Melbourne VIC 3800, Australia.
authors.forename: Chen
authors.initials: C
authors.lastname: Li
authors.affiliation: Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne VIC 3800, Australia.
authors.forename: Geoffrey I
authors.initials: GI
authors.lastname: Webb
authors.affiliation: Chemical Engineering Discipline, School of Engineering, Monash University, Jalan Lagoon Selatan 46150, Bandar Sunway, Selangor, Malaysia.|Advanced Engineering Platform, School of Engineering, Monash University, Jalan Lagoon Selatan 46150, Bandar Sunway, Selangor, Malaysia.
authors.forename: BengTi
authors.initials: B
authors.lastname: Tey
authors.affiliation: Department of Biochemistry and Molecular Biology, Monash University, Melbourne VIC 3800, Australia.|Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne VIC 3800, Australia.|National Engineering Laboratory for Industrial Enzymes, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.
authors.forename: Jiangning
authors.initials: J
authors.lastname: Song
authors.affiliation: Chemical Engineering Discipline, School of Engineering, Monash University, Jalan Lagoon Selatan 46150, Bandar Sunway, Selangor, Malaysia.|Advanced Engineering Platform, School of Engineering, Monash University, Jalan Lagoon Selatan 46150, Bandar Sunway, Selangor, Malaysia.|School of Chemistry, Monash University, Melbourne VIC 3800, Australia.
authors.forename: Ramakrishnan Nagasundara
authors.initials: RN
authors.lastname: Ramanan
mesh_terms: D000465:Algorithms; D004926:Escherichia coli / Q000378:metabolism*; D029968:Escherichia coli Proteins / Q000378:metabolism*; D020407:Internet; D000069550:Machine Learning; D019897:Periplasm / Q000378:metabolism*; D012044:Regression Analysis; D012995:Solubility
chemical_list: D029968:Escherichia coli Proteins