Prediction of protein stability changes due to single amino acid mutations
(English)Manuscript (preprint) (Other academic)
Accurate prediction of the change in protein stability due to single amino acid mutations is important for guiding site-directed mutagenesis and other protein-engineering techniques. Recently, different two state predictors became available aimed at predicting if point mutations stabilize or destabilize a protein. Considering the experimental errors and tolerances of protein with respect to mutations, we realized that the neutral mutations, which only slightly affect the protein’s stability, must be considered as well. Here, we present a new classification scheme for a three-state predictor (destabilizing, neutral and stabilizing mutations) based on multi-class support vector machines (SVM). We have created a refined training dataset of single amino acid mutations and evaluate the predictive ability of models trained on homology clustered and non-clustered training data using two different cross validation procedures. The experimental results reveal the significant difference of prediction accuracy according to different evaluation procedures. Furthermore we demonstrate that, for non-clustered model, the prediction accuracy based on the protein sequence information alone is comparable to the prediction accuracy based on protein structure information. On the other hand, for clustered model, the prediction ability is significantly improved when protein tertiary structure information is included. The comparison of prediction accuracy for the two models reveals that the prediction accuracy of mutation stability on clustered proteins is still a challenging task. Moreover, benchmarking by using previously published datasets, demonstrate that our method has an improved prediction performance over many established methods.
Bioinformatics and Systems Biology
IdentifiersURN: urn:nbn:se:umu:diva-33769OAI: oai:DiVA.org:umu-33769DiVA: diva2:317972