Multiple Property Tolerance Analysis for the Evaluation of Missense Mutations

Abstract

Computational prediction of the impact of a mutation on protein function is still not accurate enough for clinical diagnostics without additional human expert analysis. Sequence alignment-based methods have been extensively used but their results highly depend on the quality of the input alignments and the choice of sequences. Incorporating the structural information with alignments improves prediction accuracy. Here, we present a conservation of amino acid properties method for mutation prediction, Mu ltiple Properties T olerance A nalysis (MuTA), and a new strategy, MuTA/S, to incorporate the solvent accessible surface (SAS) property into MuTA. Instead of combining multiple features by machine learning or mathematical methods, an intuitive strategy is used to divide the residues of a protein into different groups, and in each group the properties used is adjusted. The results for LacI, lysozyme, and HIV protease show that MuTA performs as well as the widely used SIFT algorithm while MuTA/S outperforms SIFT and MuTA by 2%–25% in terms of prediction accuracy. By incorporating the SAS term alone, the alignment dependency of overall prediction accuracy is significantly reduced. MuTA/S also defines a new way to incorporate any structural features and knowledge and may lead to more accurate predictions.