Performance of Protein Stability Predictors

Sofia Khan (1) , Mauno A Vihinen (1)

1 Institute of Medical Technology

Stability is a fundamental property affecting function, activity, and regulation of biomolecules. Stability changes are often found for mutated proteins involved in diseases. Stability predictors computationally predict protein-stability changes caused by mutations. We performed a systematic analysis of eleven online stability predictors' performances. These predictors are CUPSAT, Dmutant, FoldX, I-Mutant2.0, two versions of I-Mutant3.0 (sequence and structure versions), MultiMutate, MUpro, SCide, Scpred, and SRide. As input, 1784 single mutations found in 80 proteins were used, and these mutations did not include those used for training. The programs' performances were also assessed according to where the mutations were found in the proteins, i.e., in secondary structures and on the surface or in the core of a protein, and according to protein structure type. The extents to which the mutations altered the occupied volumes at the residue sites and the charge interactions were also characterized. The predictions of all programs were in line with the experimental data. I-Mutant3.0 (utilizing structural information), CUPSAT, Dmutant, and FoldX were the most reliable predictors, and Scpred was the best of the stability-center predictors. However, at best, the predictions were only moderately accurate (~60%) and significantly better tools would be needed for routine analysis of mutation effects.

Mots clés: Life Sciences

Sofia Khan, Mauno A Vihinen. Performance of Protein Stability Predictors. Human Mutation, 2010, 1 (1), pp.675. ⟨10.1002/humu.21242⟩. ⟨hal-00552374⟩