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A basic question of protein structural studies is to which extent mutationsaffect the stability. This question may be addressed starting from sequenceand/or from structure. In proteomics and genomics studies prediction of proteinstability free energy change (DDG) upon single point mutation may also help theannotation process. The experimental SSG values are affected by uncertainty asmeasured by standard deviations. Most of the DDG values are nearly zero (about32% of the DDG data set ranges from -0.5 to 0.5 Kcal/mol) and both the valueand sign of DDG may be either positive or negative for the same mutationblurring the relationship among mutations and expected DDG value. In order toovercome this problem we describe a new predictor that discriminates between 3mutation classes: destabilizing mutations (DDG<-0.5 Kcal/mol), stabilizingmutations (DDG>0.5 Kcal/mol) and neutral mutations (-0.5<=DDG<=0.5 Kcal/mol).In this paper a support vector machine starting from the protein sequence orstructure discriminates between stabilizing, destabilizing and neutralmutations. We rank all the possible substitutions according to a three stateclassification system and show that the overall accuracy of our predictor is ashigh as 52% when performed starting from sequence information and 58% when theprotein structure is available, with a mean value correlation coefficient of0.30 and 0.39, respectively. These values are about 20 points per cent higherthan those of a random predictor.

A three-state prediction of single point mutations on protein stability changes