SOLpro swMATH ID: 35298 Software Authors: Magnan, C. N.; Randall, A.; Baldi, P. Description: 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 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 Homepage: https://academic.oup.com/bioinformatics/article/25/17/2200/211163 Related Software: MODELLER; ExPASy; Frustratometer; PAComplex; JCat; PROCHECK; PSIPRED Cited in: 1 Publication Cited by 5 Authors 1 Ghasemi, Younes 1 Javadi, Gholamreza 1 Khoshnoud, Mohammad Javad 1 Nezafat, Navid 1 Omidinia, Eskandar Cited in 1 Serial 1 Journal of Theoretical Biology Cited in 1 Field 1 Biology and other natural sciences (92-XX) Citations by Year