title: Protein solubility: sequence based prediction and experimental verification.
abstract: BACKGROUND
  Obtaining soluble proteins in sufficient concentrations is a recurring limiting factor in various experimental studies. Solubility is an individual trait of proteins which, under a given set of experimental conditions, is determined by their amino acid sequence. Accurate theoretical prediction of solubility from sequence is instrumental for setting priorities on targets in large-scale proteomics projects.
  
  
  RESULTS
  We present a machine-learning approach called PROSO to assess the chance of a protein to be soluble upon heterologous expression in Escherichia coli based on its amino acid composition. The classification algorithm is organized as a two-layered structure in which the output of primary support vector machine (SVM) classifiers serves as input for a secondary Naive Bayes classifier. Experimental progress information from the TargetDB database as well as previously published datasets were used as the source of training data. In comparison with previously published methods our classification algorithm possesses improved discriminatory capacity characterized by the Matthews Correlation Coefficient (MCC) of 0.434 between predicted and known solubility states and the overall prediction accuracy of 72% (75 and 68% for positive and negative class, respectively). We also provide experimental verification of our predictions using solubility measurements for 31 mutational variants of two different proteins.
authors.affiliation: Department of Genome Oriented Bioinformatics, Technische Universität München, Wissenschaftszentrum Weihenstephan, 85350 Freising, Germany.
authors.forename: Pawel
authors.initials: P
authors.lastname: Smialowski
authors.forename: Antonio J
authors.initials: AJ
authors.lastname: Martin-Galiano
authors.forename: Aleksandra
authors.initials: A
authors.lastname: Mikolajka
authors.forename: Tobias
authors.initials: T
authors.lastname: Girschick
authors.forename: Tad A
authors.initials: TA
authors.lastname: Holak
authors.forename: Dmitrij
authors.initials: D
authors.lastname: Frishman
mesh_terms: D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D003198:Computer Simulation; D008956:Models, Chemical; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods*; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods*; D012995:Solubility; D012997:Solvents / Q000737:chemistry*
chemical_list: D011506:Proteins; D012997:Solvents