Predicting protein stability changes from sequences using support vector machines

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Better prediction of functional effects for sequence variants (2015, neutral): “Some tools are focused on non-coding regions [2-4]; others focus on coding regions and predict the effects of single amino acid variants (non-synonymous single-nucleotide polymorphisms, nsSNPs, or single amino acid substitutions, SAAS) on aspects such as protein structure [5], stability [6-8], bindi”
Machine learning in protein engineering (2018, methodology): “Support vector machines have been used to predict protein thermostability (Capriotti et al., 2005b;a; Cheng et al., 2006; Buske et al., 2009; Tian et al., 2010; Jia et al., 2015; Li & Fang, 2012; Liu & Kang, 2012), enantioselectivity (Zaugg et al., 2017), and membrane protein expression (Saladi et…”
Stability effects of mutations and protein evolvability. (2009, methodology): “Some prediction methods are based on sequence [18–20], others on 3D structures [21–24].”
Improving the prediction of disease-related variants using protein three-dimensional structure (2011, methodology): “These algorithms are able to predict the effect of the mutation on protein stability [13,14,19,23-26], protein functional activity [9,21], and insurgence of human pathologies [7,8,10-12,1518,20-22].”