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Predicts the effects of amino acid substitution on protein solubility. PON-Sol is a machine learning-based method and utilizes amino acid features and evolutionary information. The predictor can distinguish both solubility decreasing and increasing variants from those not affecting solubility. PON-Sol has normalized correct prediction ratio of 0.491 on cross-validation and 0.432 for independent test set. The performance of the method was compared both to solubility and aggregation predictors and found to be superior. PON-Sol can be used for the prediction of effects of disease-related substitutions, effects on heterologous recombinant protein expression and enhanced crystallizability. One application is to investigate effects of all possible amino acid substitutions (AAs) in a protein to aid protein engineering.


A scoring function to predict solubility mutagenesis based on the frequencies of triplets of AAs that have low degrees of buriedness. We develop a training method based on linear programming (LP), which combines some features of SVM and the Lasso. This LP method allows us to impose meaningful bounds on the weights as part of the learning process. As such, we attain better performances than the standard SVM and Lasso classifiers. OptSolMut handles single- and multiple-point mutants in the same manner. In fact, it may be more accurate on multiple-point mutants, as the number of triplets involved in the mutation will typically be larger.