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SOLpro: accurate sequence-based prediction of protein solubility

2009; Oxford University Press; Volume: 25; Issue: 17 Linguagem: Inglês

10.1093/bioinformatics/btp386

1367-4811

Chr̀istophe Magnan, Arlo Randall, Pierre Baldi,

Microbial Metabolic Engineering and Bioproduction

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.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.