title: Prediction of protein solubility in Escherichia coli using logistic regression.
abstract: In this article we present a new and more accurate model for the prediction of the solubility of proteins overexpressed in the bacterium Escherichia coli. The model uses the statistical technique of logistic regression. To build this model, 32 parameters that could potentially correlate well with solubility were used. In addition, the protein database was expanded compared to those used previously. We tested several different implementations of logistic regression with varied results. The best implementation, which is the one we report, exhibits excellent overall prediction accuracies: 94% for the model and 87% by cross-validation. For comparison, we also tested discriminant analysis using the same parameters, and we obtained a less accurate prediction (69% cross-validation accuracy for the stepwise forward plus interactions model).
authors.affiliation: University of Oklahoma, Norman, 73019, USA.
authors.forename: Armando A
authors.initials: AA
authors.lastname: Diaz
authors.forename: Emanuele
authors.initials: E
authors.lastname: Tomba
authors.forename: Reese
authors.initials: R
authors.lastname: Lennarson
authors.forename: Rex
authors.initials: R
authors.lastname: Richard
authors.forename: Miguel J
authors.initials: MJ
authors.lastname: Bagajewicz
authors.forename: Roger G
authors.initials: RG
authors.lastname: Harrison
mesh_terms: D030562:Databases, Protein; D004926:Escherichia coli / Q000235:genetics*; D002479:Inclusion Bodies / Q000737:chemistry*; D016015:Logistic Models; D011994:Recombinant Proteins / Q000737:chemistry*; D012995:Solubility
chemical_list: D011994:Recombinant Proteins