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Bioinformatics Advance Access originally published online on June 23, 2009
Bioinformatics 2009 25(17):2200-2207; doi:10.1093/bioinformatics/btp386
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© The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

SOLpro: accurate sequence-based prediction of protein solubility

Christophe N. Magnan , Arlo Randall and Pierre Baldi *

Institute for Genomics and Bioinformatics, School of Information and Computer Sciences, University of California, Irvine, CA, USA

* To whom correspondence should be addressed.


  Abstract

Motivation: Protein insolubility is a major obstacle for many experimental studies. A sequence-based prediction method able to accurately predict the propensity of a protein to be soluble on overexpression could be used, for instance, to prioritize targets in large-scale proteomics projects and to identify mutations likely to increase the solubility of insoluble proteins.

Results: Here, we first curate a large, non-redundant and balanced training set of more than 17 000 proteins. Next, we extract and study 23 groups of features computed directly or predicted (e.g. secondary structure) from the primary sequence. The data and the features are used to train a two-stage support vector machine (SVM) architecture. The resulting predictor, SOLpro, is compared directly with existing methods and shows significant improvement according to standard evaluation metrics, with an overall accuracy of over 74% estimated using multiple runs of 10-fold cross-validation.

Availability: SOLpro is integrated in the SCRATCH suite of predictors and is available for download as a standalone application and as a web server at: http://scratch.proteomics.ics.uci.edu.

Contact: pfbaldi{at}ics.uci.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Burkhard Rost

Received on April 13, 2009; revised on June 9, 2009; accepted on June 17, 2009

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