SOLpro: accurate sequence-based prediction of protein solubility
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 i...
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Published in | Bioinformatics Vol. 25; no. 17; pp. 2200 - 2207 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Oxford
Oxford University Press
01.09.2009
Oxford Publishing Limited (England) |
Subjects | |
Online Access | Get full text |
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Summary: | 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@ics.uci.edu Supplementary information: Supplementary data are available at Bioinformatics online. |
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Bibliography: | To whom correspondence should be addressed. Associate Editor: Burkhard Rost istex:3386A2A21ED0B0EC10BD567F6A6E2CAF9D683121 ark:/67375/HXZ-R5FJPCG1-H ArticleID:btp386 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 ObjectType-Article-2 ObjectType-Feature-1 |
ISSN: | 1367-4803 1460-2059 1367-4811 |
DOI: | 10.1093/bioinformatics/btp386 |