title: SOLpro: accurate sequence-based prediction of protein solubility.
abstract: BACKGROUND
  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.
authors: Institute for Genomics and Bioinformatics, School of Information and Computer Sciences, University of California, Irvine, CA, USA.
authors: Christophe N
authors: CN
authors: Magnan
authors: Arlo
authors: A
authors: Randall
authors: Pierre
authors: P
authors: Baldi
mesh_terms: D001185:Artificial Intelligence; D003198:Computer Simulation; D030562:Databases, Protein; D004926:Escherichia coli; D011506:Proteins / Q000737:chemistry*; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid; D012984:Software; D012995:Solubility
chemical_list: D011506:Proteins