Author: Bikash K. Bhandari; Paul P. Gardner; Chun Shen Lim
Title: Solubility-Weighted Index: fast and accurate prediction of protein solubility Document date: 2020_2_16
ID: 2rpr7aph_2
Snippet: Protein solubility, at least in part, depends upon extrinsic factors such as ionic strength, temperature and pH, as well as intrinsic factors-the physicochemical properties of the protein sequence and structure, including molecular weight, amino acid composition, hydrophobicity, aromaticity, isoelectric point, structural propensities and the polarity of surface residues (Wilkinson and Harrison 1991; Chiti et al. 2003; Tartaglia et al. 2004; Diaz .....
Document: Protein solubility, at least in part, depends upon extrinsic factors such as ionic strength, temperature and pH, as well as intrinsic factors-the physicochemical properties of the protein sequence and structure, including molecular weight, amino acid composition, hydrophobicity, aromaticity, isoelectric point, structural propensities and the polarity of surface residues (Wilkinson and Harrison 1991; Chiti et al. 2003; Tartaglia et al. 2004; Diaz et al. 2010) . Many solubility prediction tools have been developed around these features using statistical models (e.g., linear and logistic regression) or other machine learning models (e.g., support vector machines and neural networks) (Hirose and Noguchi 2013; Habibi et al. 2014; Hebditch et al. 2017; Sormanni et al. 2017; Heckmann et al. 2018; Z. Wu et al. 2019; Yang, Wu, and Arnold 2019) .
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