Predicting protein stability changes from sequence with Support Vector Machines

Motivation: The prediction of protein stability change upon mutations is a key problem for understanding protein folding and misfolding. Presently methods are available to predict stability changes only when the atomic structure of the protein is available. Methods addressing the same task starting from the protein sequence are however necessary in order to complete genome annotation, especially in relation to single nucleotide polymorphisms (SNPs) and related diseases. Results: We develop a method based on support vector machines (SVM) the starting from the protein sequence predicts the sign and the value of free energy stability change upon single point mutation. We show that the accuracy of our predictor is as high as 77% in the specific task of predicting the ddG sign related to the corresponding protein stability. When predicting the ddG values, a satisfying correlation agreement with the experimental data is also found. As a final blind benchmark, the predictor is applied to proteins with a set of disease-related SNPs, for which thermodynamics data are also known. We found that our predictions corroborate the view that disease-related mutations correspond to decrease of protein stability. Availability: www.gpcr2.biocomp.unibo.i