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Bioinformatics Advance Access published online on December 6, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl623
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received September 25, 2006
Revised November 22, 2006
Accepted December 4, 2006

Article

Protein solubility: sequence based prediction and experimental verification

Pawel Smialowski 1, Antonio J. Martin-Galiano 1, Aleksandra Mikolajka 2, Tobias Girschick 1, Tad A. Holak 2, and Dmitrij Frishman 1 *

1 Department of Genome Oriented Bioinformatics, Technische Universität München, Wissenschaftszentrum Weihenstephan, 85350 Freising, Germany
2 Max Planck Institute for Biochemistry, 82152 Martinsried, Germany

* To whom correspondence should be addressed.
Dmitrij Frishman, E-mail: d.frishman{at}wzw.tum.de


  Abstract

Motivation: 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 E. 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 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 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.

Availability: A Web server for protein solubility prediction is available at http://webclu.bio.wzw.tum.de:8080/proso.

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Thomas Lengauer
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