Year: 2009
Type: article
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: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.
Source: Bioinformatics
Authors: Christophe N. Magnan, Arlo Randall, Pierre Baldi
Institution: University of California, Irvine
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: bronze
OpenAlex ID: W2133458779