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

A three-state prediction of single point mutations on protein stability changes,10.1186/1471-2105-9-S2-S6,BMC Bioinformatics,Emidio Capriotti,Piero Fa

A three-state prediction of single point mutations on protein stability changes   (Citations: 14)
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Background 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 (G) upon single point mutation may also help the annotation process. The experimental G values are affected by uncertainty as measured by standard deviations. Most of the G values are nearly zero (about 32% of the G data set ranges from -0.5 to 0.5 Kcal/mol) and both the value and sign of G may be either positive or negative for the same mutation blurring the relationship among mutations and expected G value. In order to overcome this problem we describe a new predictor that discriminates between 3 mutation classes: destabilizing mutations (G<-0.5 Kcal/mol), stabilizing mutations (G>0.5 Kcal/mol) and neutral mutations (-0.5G0.5 Kcal/mol). Results 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. Conclusions Our method improves the quality of the prediction of the free energy change due to single point protein mutations by adopting a hypothesis of thermodynamic reversibility of the existing experimental data. By this we both recast the thermodynamic symmetry of the problem and balance the distribution of the available experimental measurements of free energy changes. This eliminates possible overestimations of the previously described methods trained on an unbalanced data set comprising a number of destabilizing mutations higher than that of stabilising ones.
Journal: BMC Bioinformatics , vol. 9, no. S-2, pp. S6-9, 2008
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