  A basic question of protein structural studies is to which extent mutations
affect the stability. This question may be addressed starting from sequence
and/or from structure. In proteomics and genomics studies prediction of protein
stability free energy change (DDG) upon single point mutation may also help the
annotation process. The experimental SSG values are affected by uncertainty as
measured by standard deviations. Most of the DDG values are nearly zero (about
32% of the DDG data set ranges from -0.5 to 0.5 Kcal/mol) and both the value
and sign of DDG may be either positive or negative for the same mutation
blurring the relationship among mutations and expected DDG value. In order to
overcome this problem we describe a new predictor that discriminates between 3
mutation classes: destabilizing mutations (DDG<-0.5 Kcal/mol), stabilizing
mutations (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 or
structure discriminates between stabilizing, destabilizing and neutral
mutations. We rank all the possible substitutions according to a three state
classification system and show that the overall accuracy of our predictor is as
high as 52% when performed starting from sequence information and 58% when the
protein structure is available, with a mean value correlation coefficient of
0.30 and 0.39, respectively. These values are about 20 points per cent higher
than those of a random predictor.
